Artificial Intelligence Frequently Asked Questions

Artificial Intelligence Frequently Asked Questions

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Artificial Intelligence Frequently Asked Questions

AI and its related fields — such as machine learning and data science — are becoming an increasingly important parts of our lives, so it stands to reason why AI Frequently Asked Questions (FAQs)are a popular choice among many people. AI has the potential to simplify tedious and repetitive tasks while enriching our everyday lives with extraordinary insights – but at the same time, it can also be confusing and even intimidating.

This AI FAQs offer valuable insight into the mechanics of AI, helping us become better-informed about AI’s capabilities, limitations, and ethical considerations. Ultimately, AI FAQs provide us with a deeper understanding of AI as well as a platform for healthy debate.

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Artificial Intelligence Frequently Asked Questions: How do you train AI models?

Training AI models involves feeding large amounts of data to an algorithm and using that data to adjust the parameters of the model so that it can make accurate predictions. This process can be supervised, unsupervised, or semi-supervised, depending on the nature of the problem and the type of algorithm being used.

Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?

Consciousness is a complex and poorly understood phenomenon, and it is currently not possible to say whether AI will ever be conscious. Some researchers believe that it may be possible to build systems that have some form of subjective experience, while others believe that true consciousness requires biological systems.

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Artificial Intelligence Frequently Asked Questions: How do you do artificial intelligence?

Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. There are many different approaches to building AI systems, including machine learning, deep learning, and evolutionary algorithms, among others.

Artificial Intelligence Frequently Asked Questions: How do you test an AI system?

Testing an AI system involves evaluating its performance on a set of tasks and comparing its results to human performance or to a previously established benchmark. This process can be used to identify areas where the AI system needs to be improved, and to ensure that the system is safe and reliable before it is deployed in real-world applications.

Artificial Intelligence Frequently Asked Questions: Will AI rule the world?

There is no clear evidence that AI will rule the world. While AI systems have the potential to greatly impact society and change the way we live, it is unlikely that they will take over completely. AI systems are designed and programmed by humans, and their behavior is ultimately determined by the goals and values programmed into them by their creators.


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Artificial Intelligence Frequently Asked Questions:  What is artificial intelligence?

Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. The field draws on techniques from computer science, mathematics, psychology, and other disciplines to create systems that can make decisions, solve problems, and learn from experience.

Artificial Intelligence Frequently Asked Questions:   How AI will destroy humanity?

The idea that AI will destroy humanity is a popular theme in science fiction, but it is not supported by the current state of AI research. While there are certainly concerns about the potential impact of AI on society, most experts believe that these effects will be largely positive, with AI systems improving efficiency and productivity in many industries. However, it is important to be aware of the potential risks and to proactively address them as the field of AI continues to evolve.

Artificial Intelligence Frequently Asked Questions:   Can Artificial Intelligence read?

Yes, in a sense, some AI systems can be trained to recognize text and understand the meaning of words, sentences, and entire documents. This is done using techniques such as optical character recognition (OCR) for recognizing text in images, and natural language processing (NLP) for understanding and generating human-like text.

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However, the level of understanding that these systems have is limited, and they do not have the same level of comprehension as a human reader.

Artificial Intelligence Frequently Asked Questions:   What problems do AI solve?

AI can solve a wide range of problems, including image recognition, natural language processing, decision making, and prediction. AI can also help to automate manual tasks, such as data entry and analysis, and can improve efficiency and accuracy.

Artificial Intelligence Frequently Asked Questions:  How to make a wombo AI?

To make a “wombo AI,” you would need to specify what you mean by “wombo.” AI can be designed to perform various tasks and functions, so the steps to create an AI would depend on the specific application you have in mind.

Artificial Intelligence Frequently Asked Questions:   Can Artificial Intelligence go rogue?

In theory, AI could go rogue if it is programmed to optimize for a certain objective and it ends up pursuing that objective in a harmful manner. However, this is largely considered to be a hypothetical scenario and there are many technical and ethical considerations that are being developed to prevent such outcomes.

Artificial Intelligence Frequently Asked Questions:   How do you make an AI algorithm?

There is no one-size-fits-all approach to making an AI algorithm, as it depends on the problem you are trying to solve and the data you have available.

However, the general steps include defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as necessary.

Artificial Intelligence Frequently Asked Questions:   How to make AI phone case?

To make an AI phone case, you would likely need to have knowledge of electronics and programming, as well as an understanding of how to integrate AI algorithms into a device.

Artificial Intelligence Frequently Asked Questions:   Are humans better than AI?

It is not accurate to say that humans are better or worse than AI, as they are designed to perform different tasks and have different strengths and weaknesses. AI can perform certain tasks faster and more accurately than humans, while humans have the ability to reason, make ethical decisions, and have creativity.

Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?

The question of whether AI will ever be conscious is a topic of much debate and speculation within the field of AI and cognitive science. Currently, there is no consensus among experts about whether or not AI can achieve consciousness.

Consciousness is a complex and poorly understood phenomenon, and there is no agreed-upon definition or theory of what it is or how it arises.

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Some researchers believe that consciousness is a purely biological phenomenon that is dependent on the physical structure and processes of the brain, while others believe that it may be possible to create artificial systems that are capable of experiencing subjective awareness and self-reflection.

However, there is currently no known way to create a conscious AI system. While some AI systems can mimic human-like behavior and cognitive processes, they are still fundamentally different from biological organisms and lack the subjective experience and self-awareness that are thought to be essential components of consciousness.

That being said, AI technology is rapidly advancing, and it is possible that in the future, new breakthroughs in neuroscience and cognitive science could lead to the development of AI systems that are capable of experiencing consciousness.

However, it is important to note that this is still a highly speculative and uncertain area of research, and there is no guarantee that AI will ever be conscious in the same way that humans are.

Artificial Intelligence Frequently Asked Questions:   Is Excel AI?

Excel is not AI, but it can be used to perform some basic data analysis tasks, such as filtering and sorting data and creating charts and graphs.

An example of an intelligent automation solution that makes use of AI and transfers files between folders could be a system that uses machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.

What is an example of an intelligent automation solution that makes use of artificial intelligence transferring files between folders?

An example of an intelligent automation solution that uses AI to transfer files between folders could be a system that employs machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.

Artificial Intelligence Frequently Asked Questions: How do AI battles work in MK11?

The specific details of how AI battles work in MK11 are not specified, as it likely varies depending on the game’s design and programming. However, in general, AI opponents in fighting games can be designed to use a combination of pre-determined strategies and machine learning algorithms to react to the player’s actions in real-time.

Artificial Intelligence Frequently Asked Questions: Is pattern recognition a part of artificial intelligence?

Yes, pattern recognition is a subfield of artificial intelligence (AI) that involves the development of algorithms and models for identifying patterns in data. This is a crucial component of many AI systems, as it allows them to recognize and categorize objects, images, and other forms of data in real-world applications.

Artificial Intelligence Frequently Asked Questions: How do I use Jasper AI?

The specifics on how to use Jasper AI may vary depending on the specific application and platform. However, in general, using Jasper AI would involve integrating its capabilities into your system or application, and using its APIs to access its functions and perform tasks such as natural language processing, decision making, and prediction.

Artificial Intelligence Frequently Asked Questions: Is augmented reality artificial intelligence?

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Augmented reality (AR) can make use of artificial intelligence (AI) techniques, but it is not AI in and of itself. AR involves enhancing the real world with computer-generated information, while AI involves creating systems that can perform tasks that typically require human intelligence, such as image recognition, decision making, and natural language processing.

Artificial Intelligence Frequently Asked Questions: Does artificial intelligence have rights?

No, artificial intelligence (AI) does not have rights as it is not a legal person or entity. AI is a technology and does not have consciousness, emotions, or the capacity to make decisions or take actions in the same way that human beings do. However, there is ongoing discussion and debate around the ethical considerations and responsibilities involved in creating and using AI systems.

Artificial Intelligence Frequently Asked Questions: What is generative AI?

Generative AI is a branch of artificial intelligence that involves creating computer algorithms or models that can generate new data or content, such as images, videos, music, or text, that mimic or expand upon the patterns and styles of existing data.

Generative AI models are trained on large datasets using deep learning techniques, such as neural networks, and learn to generate new data by identifying and emulating patterns, structures, and relationships in the input data.

Some examples of generative AI applications include image synthesis, text generation, music composition, and even chatbots that can generate human-like conversations. Generative AI has the potential to revolutionize various fields, such as entertainment, art, design, and marketing, and enable new forms of creativity, personalization, and automation.

How important do you think generative AI will be for the future of development, in general, and for mobile? In what areas of mobile development do you think generative AI has the most potential?

Generative AI is already playing a significant role in various areas of development, and it is expected to have an even greater impact in the future. In the realm of mobile development, generative AI has the potential to bring a lot of benefits to developers and users alike.

One of the main areas of mobile development where generative AI can have a significant impact is user interface (UI) and user experience (UX) design. With generative AI, developers can create personalized and adaptive interfaces that can adjust to individual users’ preferences and behaviors in real-time. This can lead to a more intuitive and engaging user experience, which can translate into higher user retention and satisfaction rates.

Another area where generative AI can make a difference in mobile development is in content creation. Generative AI models can be used to automatically generate high-quality and diverse content, such as images, videos, and text, that can be used in various mobile applications, from social media to e-commerce.

Furthermore, generative AI can also be used to improve mobile applications’ performance and efficiency. For example, it can help optimize battery usage, reduce network latency, and improve app loading times by predicting and pre-loading content based on user behavior.

Overall, generative AI has the potential to bring significant improvements and innovations to various areas of mobile development, including UI/UX design, content creation, and performance optimization. As the technology continues to evolve, we can expect to see even more exciting applications and use cases emerge in the future.

How do you see the role of developers evolving as a result of the development and integration of generative AI technologies? How could it impact creativity, job requirements and skill sets in software development?

The development and integration of generative AI technologies will likely have a significant impact on the role of developers and the software development industry as a whole. Here are some ways in which generative AI could impact the job requirements, skill sets, and creativity of developers:

  1. New skills and knowledge requirements: As generative AI becomes more prevalent, developers will need to have a solid understanding of machine learning concepts and techniques, as well as experience with deep learning frameworks and tools. This will require developers to have a broader skill set that includes both software development and machine learning.

  2. Greater focus on data: Generative AI models require large amounts of data to be trained, which means that developers will need to have a better understanding of data collection, management, and processing. This could lead to the emergence of new job roles, such as data engineers, who specialize in preparing and cleaning data for machine learning applications.

  3. More creativity and innovation: Generative AI has the potential to unlock new levels of creativity and innovation in software development. By using AI-generated content and models, developers can focus on higher-level tasks, such as designing user experiences and optimizing software performance, which could lead to more innovative and user-friendly products.

  4. Automation of repetitive tasks: Generative AI can be used to automate many of the repetitive tasks that developers currently perform, such as writing code and testing software. This could lead to increased efficiency and productivity, allowing developers to focus on more strategic and value-added tasks.

Overall, the integration of generative AI technologies is likely to lead to a shift in the role of developers, with a greater emphasis on machine learning and data processing skills. However, it could also open up new opportunities for creativity and innovation, as well as automate many repetitive tasks, leading to greater efficiency and productivity in the software development industry.

Do you have any concerns about using generative AI in mobile development work? What are they? 

As with any emerging technology, there are potential concerns associated with the use of generative AI in mobile development. Here are some possible concerns to keep in mind:

  1. Bias and ethics: Generative AI models are trained on large datasets, which can contain biases and reinforce existing societal inequalities. This could lead to AI-generated content that reflects and perpetuates these biases, which could have negative consequences for users and society as a whole. Developers need to be aware of these issues and take steps to mitigate bias and ensure ethical use of AI in mobile development.

  2. Quality control: While generative AI can automate the creation of high-quality content, there is a risk that the content generated may not meet the required standards or be appropriate for the intended audience. Developers need to ensure that the AI-generated content is of sufficient quality and meets user needs and expectations.

  3. Security and privacy: Generative AI models require large amounts of data to be trained, which raises concerns around data security and privacy. Developers need to ensure that the data used to train the AI models is protected and that user privacy is maintained.

  4. Technical limitations: Generative AI models are still in the early stages of development, and there are limitations to what they can achieve. For example, they may struggle to generate content that is highly specific or nuanced. Developers need to be aware of these limitations and ensure that generative AI is used appropriately in mobile development.

Overall, while generative AI has the potential to bring many benefits to mobile development, developers need to be aware of the potential concerns and take steps to mitigate them. By doing so, they can ensure that the AI-generated content is of high quality, meets user needs, and is developed in an ethical and responsible manner.

Artificial Intelligence Frequently Asked Questions: How do you make an AI engine?

Making an AI engine involves several steps, including defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as needed. The specific approach and technologies used will depend on the problem you are trying to solve and the type of AI system you are building. In general, developing an AI engine requires knowledge of computer science, mathematics, and machine learning algorithms.

Artificial Intelligence Frequently Asked Questions: Which exclusive online concierge service uses artificial intelligence to anticipate the needs and tastes of travellers by analyzing their spending patterns?

There are a number of travel and hospitality companies that are exploring the use of AI to provide personalized experiences and services to their customers based on their preferences, behavior, and spending patterns.

Artificial Intelligence Frequently Asked Questions: How to validate an artificial intelligence?

To validate an artificial intelligence system, various testing methods can be used to evaluate its performance, accuracy, and reliability. This includes data validation, benchmarking against established models, testing against edge cases, and validating the output against known outcomes. It is also important to ensure the system is ethical, transparent, and accountable.

Artificial Intelligence Frequently Asked Questions: When leveraging artificial intelligence in today’s business?

When leveraging artificial intelligence in today’s business, companies can use AI to streamline processes, gain insights from data, and automate tasks. AI can also help improve customer experience, personalize offerings, and reduce costs. However, it is important to ensure that the AI systems used are ethical, secure, and transparent.

Artificial Intelligence Frequently Asked Questions: How are the ways AI learns similar to how you learn?

AI learns in a similar way to how humans learn through experience and repetition. Like humans, AI algorithms can recognize patterns, make predictions, and adjust their behavior based on feedback. However, AI is often able to process much larger volumes of data at a much faster rate than humans.

Artificial Intelligence Frequently Asked Questions: What is the fear of AI?

The fear of AI, often referred to as “AI phobia” or “AI anxiety,” is the concern that artificial intelligence could pose a threat to humanity. Some worry that AI could become uncontrollable, make decisions that harm humans, or even take over the world.

However, many experts argue that these fears are unfounded and that AI is just a tool that can be used for good or bad depending on how it is implemented.

Artificial Intelligence Frequently Asked Questions: How have developments in AI so far affected our sense of what it means to be human?

Developments in AI have raised questions about what it means to be human, particularly in terms of our ability to think, learn, and create.

Some argue that AI is simply an extension of human intelligence, while others worry that it could eventually surpass human intelligence and create a new type of consciousness.

Artificial Intelligence Frequently Asked Questions: How to talk to artificial intelligence?

To talk to artificial intelligence, you can use a chatbot or a virtual assistant such as Siri or Alexa. These systems can understand natural language and respond to your requests, questions, and commands. However, it is important to remember that these systems are limited in their ability to understand context and may not always provide accurate or relevant responses.

Artificial Intelligence Frequently Asked Questions: How to program an AI robot?

To program an AI robot, you will need to use specialized programming languages such as Python, MATLAB, or C++. You will also need to have a strong understanding of robotics, machine learning, and computer vision. There are many resources available online that can help you learn how to program AI robots, including tutorials, courses, and forums.

Artificial Intelligence Frequently Asked Questions: Will artificial intelligence take away jobs?

Artificial intelligence has the potential to automate many jobs that are currently done by humans. However, it is also creating new jobs in fields such as data science, machine learning, and robotics. Many experts believe that while some jobs may be lost to automation, new jobs will be created as well.

Which type of artificial intelligence can repeatedly perform tasks?

The type of artificial intelligence that can repeatedly perform tasks is called narrow or weak AI. This type of AI is designed to perform a specific task, such as playing chess or recognizing images, and is not capable of general intelligence or human-like reasoning.

Artificial Intelligence Frequently Asked Questions: Has any AI become self-aware?

No, there is currently no evidence that any AI has become self-aware in the way that humans are. While some AI systems can mimic human-like behavior and conversation, they do not have consciousness or true self-awareness.

Artificial Intelligence Frequently Asked Questions: What company is at the forefront of artificial intelligence?

Several companies are at the forefront of artificial intelligence, including Google, Microsoft, Amazon, and Facebook. These companies have made significant investments in AI research and development

Artificial Intelligence Frequently Asked Questions: Which is the best AI system?

There is no single “best” AI system as it depends on the specific use case and the desired outcome. Some popular AI systems include IBM Watson, Google Cloud AI, and Microsoft Azure AI, each with their unique features and capabilities.

Artificial Intelligence Frequently Asked Questions: Have we created true artificial intelligence?

There is still debate among experts as to whether we have created true artificial intelligence or AGI (artificial general intelligence) yet.

While AI has made significant progress in recent years, it is still largely task-specific and lacks the broad cognitive abilities of human beings.

What is one way that IT services companies help clients ensure fairness when applying artificial intelligence solutions?

IT services companies can help clients ensure fairness when applying artificial intelligence solutions by conducting a thorough review of the data sets used to train the AI algorithms. This includes identifying potential biases and correcting them to ensure that the AI outputs are fair and unbiased.

Artificial Intelligence Frequently Asked Questions: How to write artificial intelligence?

To write artificial intelligence, you need to have a strong understanding of programming languages, data science, machine learning, and computer vision. There are many libraries and tools available, such as TensorFlow and Keras, that make it easier to write AI algorithms.

How is a robot with artificial intelligence like a baby?

A robot with artificial intelligence is like a baby in that both learn and adapt through experience. Just as a baby learns by exploring its environment and receiving feedback from caregivers, an AI robot learns through trial and error and adjusts its behavior based on the results.

Artificial Intelligence Frequently Asked Questions: Is artificial intelligence STEM?

Yes, artificial intelligence is a STEM (science, technology, engineering, and mathematics) field. AI requires a deep understanding of computer science, mathematics, and statistics to develop algorithms and train models.

Will AI make artists obsolete?

While AI has the potential to automate certain aspects of the creative process, such as generating music or creating visual art, it is unlikely to make artists obsolete. AI-generated art still lacks the emotional depth and unique perspective of human-created art.

Why do you like artificial intelligence?

Many people are interested in AI because of its potential to solve complex problems, improve efficiency, and create new opportunities for innovation and growth.

What are the main areas of research in artificial intelligence?

Artificial intelligence research covers a wide range of areas, including natural language processing, computer vision, machine learning, robotics, expert systems, and neural networks. Researchers in AI are also exploring ways to improve the ethical and social implications of AI systems.

How are the ways AI learn similar to how you learn?

Like humans, AI learns through experience and trial and error. AI algorithms use data to train and adjust their models, similar to how humans learn from feedback and make adjustments based on their experiences. However, AI learning is typically much faster and more precise than human learning.

Do artificial intelligence have feelings?

Artificial intelligence does not have emotions or feelings as it is a machine and lacks the capacity for subjective experiences. AI systems are designed to perform specific tasks and operate within the constraints of their programming and data inputs.

Artificial Intelligence Frequently Asked Questions: Will AI be the end of humanity?

There is no evidence to suggest that AI will be the end of humanity. While there are concerns about the ethical and social implications of AI, experts agree that the technology has the potential to bring many benefits and solve complex problems. It is up to humans to ensure that AI is developed and used in a responsible and ethical manner.

Which business case is better solved by Artificial Intelligence AI than conventional programming which business case is better solved by Artificial Intelligence AI than conventional programming?

Business cases that involve large amounts of data and require complex decision-making are often better suited for AI than conventional programming.

For example, AI can be used in areas such as financial forecasting, fraud detection, supply chain optimization, and customer service to improve efficiency and accuracy.

Who is the most powerful AI?

It is difficult to determine which AI system is the most powerful, as the capabilities of AI vary depending on the specific task or application. However, some of the most well-known and powerful AI systems include IBM Watson, Google Assistant, Amazon Alexa, and Tesla’s Autopilot system.

Have we achieved artificial intelligence?

While AI has made significant progress in recent years, we have not achieved true artificial general intelligence (AGI), which is a machine capable of learning and reasoning in a way that is comparable to human cognition. However, AI has become increasingly sophisticated and is being used in a wide range of applications and industries.

What are benefits of AI?

The benefits of AI include increased efficiency and productivity, improved accuracy and precision, cost savings, and the ability to solve complex problems.

AI can also be used to improve healthcare, transportation, and other critical areas, and has the potential to create new opportunities for innovation and growth.

How scary is Artificial Intelligence?

AI can be scary if it is not developed or used in an ethical and responsible manner. There are concerns about the potential for AI to be used in harmful ways or to perpetuate biases and inequalities. However, many experts believe that the benefits of AI outweigh the risks, and that the technology can be used to address many of the world’s most pressing problems.

How to make AI write a script?

There are different ways to make AI write a script, such as training it with large datasets, using natural language processing (NLP) and generative models, or using pre-existing scriptwriting software that incorporates AI algorithms.

How do you summon an entity without AI bedrock?

Attempting to summon entities can be dangerous and potentially harmful.

What should I learn for AI?

To work in artificial intelligence, it is recommended to have a strong background in computer science, mathematics, statistics, and machine learning. Familiarity with programming languages such as Python, Java, and C++ can also be beneficial.

Will AI take over the human race?

No, the idea of AI taking over the human race is a common trope in science fiction but is not supported by current AI capabilities. While AI can be powerful and influential, it does not have the ability to take over the world or control humanity.

Where do we use AI?

AI is used in a wide range of fields and industries, such as healthcare, finance, transportation, manufacturing, and entertainment. Examples of AI applications include image and speech recognition, natural language processing, autonomous vehicles, and recommendation systems.

Who invented AI?

The development of AI has involved contributions from many researchers and pioneers. Some of the key figures in AI history include John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who are considered to be the founders of the field.

Is AI improving?

Yes, AI is continuously improving as researchers and developers create more sophisticated algorithms, use larger and more diverse datasets, and design more advanced hardware. However, there are still many challenges and limitations to be addressed in the development of AI.

Will artificial intelligence take over the world?

No, the idea of AI taking over the world is a popular science fiction trope but is not supported by current AI capabilities. AI systems are designed and controlled by humans and are not capable of taking over the world or controlling humanity.

Is there an artificial intelligence system to help the physician in selecting a diagnosis?

Yes, there are AI systems designed to assist physicians in selecting a diagnosis by analyzing patient data and medical records. These systems use machine learning algorithms and natural language processing to identify patterns and suggest possible diagnoses. However, they are not intended to replace human expertise and judgement.

Will AI replace truck drivers?

AI has the potential to automate certain aspects of truck driving, such as navigation and safety systems. However, it is unlikely that AI will completely replace truck drivers in the near future. Human drivers are still needed to handle complex situations and make decisions based on context and experience.

How AI can destroy the world?

There is a hypothetical concern that AI could cause harm to humans in various ways. For example, if an AI system becomes more intelligent than humans, it could act against human interests or even decide to eliminate humanity. This scenario is known as an existential risk, but many experts believe it to be unlikely. To prevent this kind of risk, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.

What do you call the commonly used AI technology for learning input to output mappings?

The commonly used AI technology for learning input to output mappings is called a neural network. It is a type of machine learning algorithm that is modeled after the structure of the human brain. Neural networks are trained using a large dataset, which allows them to learn patterns and relationships in the data. Once trained, they can be used to make predictions or classifications based on new input data.

What are 3 benefits of AI?

Three benefits of AI are:

  • Efficiency: AI systems can process vast amounts of data much faster than humans, allowing for more efficient and accurate decision-making.
  • Personalization: AI can be used to create personalized experiences for users, such as personalized recommendations in e-commerce or personalized healthcare treatments.
  • Safety: AI can be used to improve safety in various applications, such as autonomous vehicles or detecting fraudulent activities in banking.

What is an artificial intelligence company?

An artificial intelligence (AI) company is a business that specializes in developing and applying AI technologies. These companies use machine learning, deep learning, natural language processing, and other AI techniques to build products and services that can automate tasks, improve decision-making, and provide new insights into data.

Examples of AI companies include Google, Amazon, and IBM.

What does AI mean in tech?

In tech, AI stands for artificial intelligence. AI is a field of computer science that aims to create machines that can perform tasks that would typically require human intelligence, such as learning, reasoning, problem-solving, and language understanding. AI techniques can be used in various applications, such as virtual assistants, chatbots, autonomous vehicles, and healthcare.

Can AI destroy humans?

There is no evidence to suggest that AI can or will destroy humans. While there are concerns about the potential risks of AI, most experts believe that AI systems will only act in ways that they have been programmed to.

To mitigate any potential risks, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.

What types of problems can AI solve?

AI can solve a wide range of problems, including:

  • Classification: AI can be used to classify data into categories, such as spam detection in email or image recognition in photography.
  • Prediction: AI can be used to make predictions based on data, such as predicting stock prices or diagnosing diseases.
  • Optimization: AI can be used to optimize systems or processes, such as scheduling routes for delivery trucks or maximizing production in a factory.
  • Natural language processing: AI can be used to understand and process human language, such as voice recognition or language translation.

Is AI slowing down?

There is no evidence to suggest that AI is slowing down. In fact, the field of AI is rapidly evolving and advancing, with new breakthroughs and innovations being made all the time. From natural language processing and computer vision to robotics and machine learning, AI is making significant strides in many areas.

How to write a research paper on artificial intelligence?

When writing a research paper on artificial intelligence, it’s important to start with a clear research question or thesis statement. You should then conduct a thorough literature review to gather relevant sources and data to support your argument. After analyzing the data, you can present your findings and draw conclusions, making sure to discuss the implications of your research and future directions for the field.

How to get AI to read text?

To get AI to read text, you can use natural language processing (NLP) techniques such as text analysis and sentiment analysis. These techniques involve training AI algorithms to recognize patterns in written language, enabling them to understand the meaning of words and phrases in context. Other methods of getting AI to read text include optical character recognition (OCR) and speech-to-text technology.

How to create your own AI bot?

To create your own AI bot, you can use a variety of tools and platforms such as Microsoft Bot Framework, Dialogflow, or IBM Watson.

These platforms provide pre-built libraries and APIs that enable you to easily create, train, and deploy your own AI chatbot or virtual assistant. You can customize your bot’s functionality, appearance, and voice, and train it to respond to specific user queries and actions.

What is AI according to Elon Musk?

According to Elon Musk, AI is “the next stage in human evolution” and has the potential to be both a great benefit and a major threat to humanity.

He has warned about the dangers of uncontrolled AI development and has called for greater regulation and oversight in the field. Musk has also founded several companies focused on AI development, such as OpenAI and Neuralink.

How do you program Artificial Intelligence?

Programming artificial intelligence typically involves using machine learning algorithms to train the AI system to recognize patterns and make predictions based on data. This involves selecting a suitable machine learning model, preprocessing the data, selecting appropriate features, and tuning the model hyperparameters.

Once the model is trained, it can be integrated into a larger software application or system to perform various tasks such as image recognition or natural language processing.

What is the first step in the process of AI?

The first step in the process of AI is to define the problem or task that the AI system will be designed to solve. This involves identifying the specific requirements, constraints, and objectives of the system, and determining the most appropriate AI techniques and algorithms to use.

Other key steps in the process include data collection, preprocessing, feature selection, model training and evaluation, and deployment and maintenance of the AI system.

How to make an AI that can talk?

One way to make an AI that can talk is to use a natural language processing (NLP) system. NLP is a field of AI that focuses on how computers can understand, interpret, and respond to human language. By using machine learning algorithms, the AI can learn to recognize speech, process it, and generate a response in a natural-sounding way.

Another approach is to use a chatbot framework, which involves creating a set of rules and responses that the AI can use to interact with users.

How to use the AI Qi tie?

The AI Qi tie is a type of smart wearable device that uses artificial intelligence to provide various functions, including health monitoring, voice control, and activity tracking. To use it, you would first need to download the accompanying mobile app, connect the device to your smartphone, and set it up according to the instructions provided.

From there, you can use voice commands to control various functions of the device, such as checking your heart rate, setting reminders, and playing music.

Is sentient AI possible?

While there is ongoing research into creating AI that can exhibit human-like cognitive abilities, including sentience, there is currently no clear evidence that sentient AI is possible or exists. The concept of sentience, which involves self-awareness and subjective experience, is difficult to define and even more challenging to replicate in a machine. Some experts believe that true sentience in AI may be impossible, while others argue that it is only a matter of time before machines reach this level of intelligence.

Is Masteron an AI?

No, Masteron is not an AI. It is a brand name for a steroid hormone called drostanolone. AI typically stands for “artificial intelligence,” which refers to machines and software that can simulate human intelligence and perform tasks that would normally require human intelligence to complete.

Is the Lambda AI sentient?

There is no clear evidence that the Lambda AI, or any other AI system for that matter, is sentient. Sentience refers to the ability to experience subjective consciousness, which is not currently understood to be replicable in machines. While AI systems can be programmed to simulate a wide range of cognitive abilities, including learning, problem-solving, and decision-making, they are not currently believed to possess subjective awareness or consciousness.

Where is artificial intelligence now?

Artificial intelligence is now a pervasive technology that is being used in many different industries and applications around the world. From self-driving cars and virtual assistants to medical diagnosis and financial trading, AI is being employed to solve a wide range of problems and improve human performance. While there are still many challenges to overcome in the field of AI, including issues related to bias, ethics, and transparency, the technology is rapidly advancing and is expected to play an increasingly important role in our lives in the years to come.

What is the correct sequence of artificial intelligence trying to imitate a human mind?

The correct sequence of artificial intelligence trying to imitate a human mind can vary depending on the specific approach and application. However, some common steps in this process may include collecting and analyzing data, building a model or representation of the human mind, training the AI system using machine learning algorithms, and testing and refining the system to improve its accuracy and performance. Other important considerations in this process may include the ethical implications of creating machines that can mimic human intelligence.

How do I make machine learning AI?

To make machine learning AI, you will need to have knowledge of programming languages such as Python and R, as well as knowledge of machine learning algorithms and tools. Some steps to follow include gathering and cleaning data, selecting an appropriate algorithm, training the algorithm on the data, testing and validating the model, and deploying it for use.

What is AI scripting?

AI scripting is a process of developing scripts that can automate the behavior of AI systems. It involves writing scripts that govern the AI’s decision-making process and its interactions with users or other systems. These scripts are often written in programming languages such as Python or JavaScript and can be used in a variety of applications, including chatbots, virtual assistants, and intelligent automation tools.

Is IOT artificial intelligence?

No, the Internet of Things (IoT) is not the same as artificial intelligence (AI). IoT refers to the network of physical devices, vehicles, home appliances, and other items that are embedded with electronics, sensors, and connectivity, allowing them to connect and exchange data. AI, on the other hand, involves the creation of intelligent machines that can learn and perform tasks that would normally require human intelligence, such as speech recognition, decision-making, and language translation.

What problems will Ai solve?

AI has the potential to solve a wide range of problems across different industries and domains. Some of the problems that AI can help solve include automating repetitive or dangerous tasks, improving efficiency and productivity, enhancing decision-making and problem-solving, detecting fraud and cybersecurity threats, predicting outcomes and trends, and improving customer experience and personalization.

Who wrote papers on the simulation of human thinking problem solving and verbal learning that marked the beginning of the field of artificial intelligence?

The papers on the simulation of human thinking, problem-solving, and verbal learning that marked the beginning of the field of artificial intelligence were written by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in the late 1950s.

The papers, which were presented at the Dartmouth Conference in 1956, proposed the idea of developing machines that could simulate human intelligence and perform tasks that would normally require human intelligence.

Given the fast development of AI systems, how soon do you think AI systems will become 100% autonomous?

It’s difficult to predict exactly when AI systems will become 100% autonomous, as there are many factors that could affect this timeline. However, it’s important to note that achieving 100% autonomy may not be possible or desirable in all cases, as there will likely always be a need for some degree of human oversight and control.

That being said, AI systems are already capable of performing many tasks autonomously, and their capabilities are rapidly expanding. For example, there are already AI systems that can drive cars, detect fraud, and diagnose diseases with a high degree of accuracy.

However, there are still many challenges to be overcome before AI systems can be truly autonomous in all domains. One of the main challenges is developing AI systems that can understand and reason about complex, real-world situations, as opposed to just following pre-programmed rules or learning from data.

Another challenge is ensuring that AI systems are safe, transparent, and aligned with human values and objectives.

This is particularly important as AI systems become more powerful and influential, and have the potential to impact many aspects of our lives.

For low-level domain-specific jobs such as industrial manufacturing, we already have Artificial Intelligence Systems that are fully autonomous, i.e., accomplish tasks without human intervention.

But those autonomous systems require collections of various intelligent skills to tackle many unseen situations; IMO, it will take a while to design one.

The major hurdle in making an A.I. autonomous system is to design an algorithm that can handle unpredictable events correctly. For a closed environment, it may not be a big issue. But for an open-ended system, the infinite number of possibilities is difficult to cover and ensure the autonomous device’s reliability.

Artificial Intelligence Frequently Asked Questions: AI Autonomous Systems

Current SOTA Artificial Intelligence algorithms are mostly data-centric training. The issue is not only the algorithm itself. The selection, generation, and pre-processing of datasets also determine the final performance of the accuracy. Machine Learning helps offload us without needing to explicitly derive the procedural methods to solve a problem. Still, it relies heavily on the input and feedback methods we need to provide correctly. Overcoming one problem might create many new ones, and sometimes, we do not even know whether the dataset is adequate, reasonable, and practical.

Overall, it’s difficult to predict exactly when AI systems will become 100% autonomous, but it’s clear that the development of AI technology will continue to have a profound impact on many aspects of our society and economy.

Will ChatGPT replace programmers?

Is it possible that ChatGPT will eventually replace programmers? The answer to this question is not a simple yes or no, as it depends on the rate of development and improvement of AI tools like ChatGPT.

If AI tools continue to advance at the same rate over the next 10 years, then they may not be able to fully replace programmers. However, if these tools continue to evolve and learn at an accelerated pace, then it is possible that they may replace at least 30% of programmers.

Although the current version of ChatGPT has some limitations and is only capable of generating boilerplate code and identifying simple bugs, it is a starting point for what is to come. With the ability to learn from millions of mistakes at a much faster rate than humans, future versions of AI tools may be able to produce larger code blocks, work with mid-sized projects, and even handle QA of software output.

In the future, programmers may still be necessary to provide commands to the AI tools, review the final code, and perform other tasks that require human intuition and judgment. However, with the use of AI tools, one developer may be able to accomplish the tasks of multiple developers, leading to a decrease in the number of programming jobs available.

In conclusion, while it is difficult to predict the extent to which AI tools like ChatGPT will impact the field of programming, it is clear that they will play an increasingly important role in the years to come.

ChatGPT is not designed to replace programmers.

While AI language models like ChatGPT can generate code and help automate certain programming tasks, they are not capable of replacing the skills, knowledge, and creativity of human programmers.

Programming is a complex and creative field that requires a deep understanding of computer science principles, problem-solving skills, and the ability to think critically and creatively. While AI language models like ChatGPT can assist in certain programming tasks, such as generating code snippets or providing suggestions, they cannot replace the human ability to design, develop, and maintain complex software systems.

Furthermore, programming involves many tasks that require human intuition and judgment, such as deciding on the best approach to solve a problem, optimizing code for efficiency and performance, and debugging complex systems. While AI language models can certainly be helpful in some of these tasks, they are not capable of fully replicating the problem-solving abilities of human programmers.

Overall, while AI language models like ChatGPT will undoubtedly have an impact on the field of programming, they are not designed to replace programmers, but rather to assist and enhance their abilities.

Artificial Intelligence Frequently Asked Questions: Machine Learning

What does a responsive display ad use in its machine learning model?

A responsive display ad uses various machine learning models such as automated targeting, bidding, and ad creation to optimize performance and improve ad relevance. It also uses algorithms to predict which ad creative and format will work best for each individual user and the context in which they are browsing.

What two things are marketers realizing as machine learning becomes more widely used?

Marketers are realizing the benefits of machine learning in improving efficiency and accuracy in various aspects of their work, including targeting, personalization, and data analysis. They are also realizing the importance of maintaining transparency and ethical considerations in the use of machine learning and ensuring it aligns with their marketing goals and values.

Artificial Intelligence Frequently Asked Questions: AWS Machine Learning Certification Specialty Exam Prep Book

How does statistics fit into the area of machine learning?

Statistics is a fundamental component of machine learning, as it provides the mathematical foundations for many of the algorithms and models used in the field. Statistical methods such as regression, clustering, and hypothesis testing are used to analyze data and make predictions based on patterns and trends in the data.

Is Machine Learning weak AI?

Yes, machine learning is considered a form of weak artificial intelligence, as it is focused on specific tasks and does not possess general intelligence or consciousness. Machine learning models are designed to perform a specific task based on training data and do not have the ability to think, reason, or learn outside of their designated task.

When evaluating machine learning results, should I always choose the fastest model?

No, the speed of a machine learning model is not the only factor to consider when evaluating its performance. Other important factors include accuracy, complexity, and interpretability. It is important to choose a model that balances these factors based on the specific needs and goals of the task at hand.

How do you learn machine learning?

You can learn machine learning through a combination of self-study, online courses, and practical experience. Some popular resources for learning machine learning include online courses on platforms such as Coursera and edX, textbooks and tutorials, and practical experience through projects and internships.

It is important to have a strong foundation in mathematics, programming, and statistics to succeed in the field.

What are your thoughts on artificial intelligence and machine learning?

Artificial intelligence and machine learning have the potential to revolutionize many aspects of society and have already shown significant impacts in various industries.

It is important to continue to develop these technologies responsibly and with ethical considerations to ensure they align with human values and benefit society as a whole.

Which AWS service enables you to build the workflows that are required for human review of machine learning predictions?

Amazon SageMaker Ground Truth is an AWS service that enables you to build workflows for human review of machine learning predictions.

This service provides an easy-to-use interface for creating and managing custom workflows and provides built-in tools for data labeling and quality control to ensure high-quality training data.

What is augmented machine learning?

Augmented machine learning is a combination of human expertise and machine learning models to improve the accuracy of machine learning. This technique is used when the available data is not enough or is not of good quality. The human expert is involved in the training and validation of the machine learning model to improve its accuracy.

Which actions are performed during the prepare the data step of workflow for analyzing the data with Oracle machine learning?

The ‘prepare the data’ step in Oracle machine learning workflow involves data cleaning, feature selection, feature engineering, and data transformation. These actions are performed to ensure that the data is ready for analysis, and that the machine learning model can effectively learn from the data.

What type of machine learning algorithm would you use to allow a robot to walk in various unknown terrains?

A reinforcement learning algorithm would be appropriate for this task. In this type of machine learning, the robot would interact with its environment and receive rewards for positive outcomes, such as moving forward or maintaining balance. The algorithm would learn to maximize these rewards and gradually improve its ability to navigate through different terrains.

Are evolutionary algorithms machine learning?

Yes, evolutionary algorithms are a subset of machine learning. They are a type of optimization algorithm that uses principles from biological evolution to search for the best solution to a problem.

Evolutionary algorithms are often used in problems where traditional optimization algorithms struggle, such as in complex, nonlinear, and multi-objective optimization problems.

Is MPC machine learning?

Yes, Model Predictive Control (MPC) is a type of machine learning. It is a feedback control algorithm that predicts the future behavior of a system and uses this prediction to optimize its performance. MPC is used in a variety of applications, including industrial control, robotics, and autonomous vehicles.

When do you use ML model?

You would use a machine learning model when you need to make predictions or decisions based on data. Machine learning models are trained on historical data and use this knowledge to make predictions on new data. Common applications of machine learning include fraud detection, recommendation systems, and image recognition.

When preparing the dataset for your machine learning model, you should use one hot encoding on what type of data?

One hot encoding is used on categorical data. Categorical data is non-numeric data that has a limited number of possible values, such as color or category. One hot encoding is a technique used to convert categorical data into a format that can be used in machine learning models. It converts each category into a binary vector, where each vector element corresponds to a unique category.

Is machine learning just brute force?

No, machine learning is not just brute force. Although machine learning models can be complex and require significant computing power, they are not simply brute force algorithms. Machine learning involves the use of statistical techniques and mathematical models to learn from data and make predictions. Machine learning is designed to make use of the available data in an efficient way, without the need for exhaustive search or brute force techniques.

How to implement a machine learning paper?

Implementing a machine learning paper involves understanding the research paper’s theoretical foundation, reproducing the results, and applying the approach to the new data to evaluate the approach’s efficacy. The implementation process begins with comprehending the paper’s theoretical framework, followed by testing and reproducing the findings to validate the approach.

Finally, the approach can be implemented on new datasets to assess its accuracy and generalizability. It’s essential to understand the mathematical concepts and programming tools involved in the paper to successfully implement the machine learning paper.

What are some use cases where more traditional machine learning models may make much better predictions than DNNS?

More traditional machine learning models may outperform deep neural networks (DNNs) in the following use cases:

  • When the dataset is relatively small and straightforward, traditional machine learning models, such as logistic regression, may be more accurate than DNNs.
  • When the dataset is sparse or when the number of observations is small, DNNs may require more computational resources and more time to train than traditional machine learning models.
  • When the problem is not complex, and the data has a low level of noise, traditional machine learning models may outperform DNNs.

Who is the supervisor in supervised machine learning?

In supervised machine learning, the supervisor refers to the algorithm that acts as the teacher or the guide to the model. The supervisor provides the model with labeled examples to train on, and the model uses these labeled examples to learn how to classify new data. The supervisor algorithm determines the accuracy of the model’s predictions, and the model is trained to minimize the difference between its predicted outputs and the known outputs.

How do you make machine learning in scratch?

To make machine learning in scratch, you need to follow these steps:

  • Choose a problem to solve and collect a dataset that represents the problem you want to solve.
  • Preprocess and clean the data to ensure that it’s formatted correctly and ready for use in a machine learning model.
  • Select a machine learning algorithm, such as decision trees, support vector machines, or neural networks.
  • Implement the selected machine learning algorithm from scratch, using a programming language such as Python or R.
  • Train the model using the preprocessed dataset and the implemented algorithm.
  • Test the accuracy of the model and evaluate its performance.

Is unsupervised learning machine learning?

Yes, unsupervised learning is a type of machine learning. In unsupervised learning, the model is not given labeled data to learn from. Instead, the model must find patterns and relationships in the data on its own. Unsupervised learning algorithms include clustering, anomaly detection, and association rule mining. The model learns from the features in the dataset to identify underlying patterns or groups, which can then be used for further analysis or prediction.

How do I apply machine learning?

Machine learning can be applied to a wide range of problems and scenarios, but the basic process typically involves:

  • gathering and preprocessing data,
  • selecting an appropriate model or algorithm,
  • training the model on the data, testing and evaluating the model, and then using the trained model to make predictions or perform other tasks on new data.
  • The specific steps and techniques involved in applying machine learning will depend on the particular problem or application.

Is machine learning possible?

Yes, machine learning is possible and has already been successfully applied to a wide range of problems in various fields such as healthcare, finance, business, and more.

Machine learning has advanced rapidly in recent years, thanks to the availability of large datasets, powerful computing resources, and sophisticated algorithms.

Is machine learning the future?

Many experts believe that machine learning will continue to play an increasingly important role in shaping the future of technology and society.

As the amount of data available continues to grow and computing power increases, machine learning is likely to become even more powerful and capable of solving increasingly complex problems.

How to combine multiple features in machine learning?

In machine learning, multiple features can be combined in various ways depending on the particular problem and the type of model or algorithm being used.

One common approach is to concatenate the features into a single vector, which can then be fed into the model as input. Other techniques, such as feature engineering or dimensionality reduction, can also be used to combine or transform features to improve performance.

Which feature lets you discover machine learning assets in Watson Studio 1 point?

The feature in Watson Studio that lets you discover machine learning assets is called the Asset Catalog.

The Asset Catalog provides a unified view of all the assets in your Watson Studio project, including data assets, models, notebooks, and other resources.

You can use the Asset Catalog to search, filter, and browse through the assets, and to view metadata and details about each asset.

What is N in machine learning?

In machine learning, N is a common notation used to represent the number of instances or data points in a dataset.

N can be used to refer to the total number of examples in a dataset, or the number of examples in a particular subset or batch of the data.

N is often used in statistical calculations, such as calculating means or variances, or in determining the size of training or testing sets.

Is VAR machine learning?

VAR, or vector autoregression, is a statistical technique that models the relationship between multiple time series variables. While VAR involves statistical modeling and prediction, it is not generally considered a form of machine learning, which typically involves using algorithms to learn patterns or relationships in data automatically without explicit statistical modeling.

How many categories of machine learning are generally said to exist?

There are generally three categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, the algorithm is trained on labeled data to make predictions or classifications. The algorithm is trained on unlabeled data to identify patterns or structure.

In reinforcement learning, the algorithm learns to make decisions and take actions based on feedback from the environment.

How to use timestamp in machine learning?

Timestamps can be used in machine learning to analyze time series data. This involves capturing data over a period of time and making predictions about future events. Time series data can be used to detect patterns, trends, and anomalies that can be used to make predictions about future events. The timestamps can be used to group data into regular intervals for analysis or used as input features for machine learning models.

Is classification a machine learning technique?

Yes, classification is a machine learning technique. It involves predicting the category of a new observation based on a training dataset of labeled observations. Classification is a supervised learning technique where the output variable is categorical. Common examples of classification tasks include image recognition, spam detection, and sentiment analysis.

Which datatype is used to teach a machine learning ML algorithms during structured learning?

The datatype used to teach machine learning algorithms during structured learning is typically a labeled dataset. This is a dataset where each observation has a known output variable. The input variables are used to train the machine learning algorithm to predict the output variable. Labeled datasets are commonly used in supervised learning tasks such as classification and regression.

How is machine learning model in production used?

A machine learning model in production is used to make predictions on new, unseen data. The model is typically deployed as an API that can be accessed by other systems or applications. When a new observation is provided to the model, it generates a prediction based on the patterns it has learned from the training data. Machine learning models in production must be continuously monitored and updated to ensure their accuracy and performance.

What are the main advantages and disadvantages of Gans over standard machine learning models?

The main advantage of Generative Adversarial Networks (GANs) over standard machine learning models is their ability to generate new data that closely resembles the training data. This makes them well-suited for applications such as image and video generation. However, GANs can be more difficult to train than other machine learning models and require large amounts of training data. They can also be more prone to overfitting and may require more computing resources to train.

How does machine learning deal with biased data?

Machine learning models can be affected by biased data, leading to unfair or inaccurate predictions. To mitigate this, various techniques can be used, such as collecting a diverse dataset, selecting unbiased features, and analyzing the model’s outputs for bias. Additionally, techniques such as oversampling underrepresented classes, changing the cost function to focus on minority classes, and adjusting the decision threshold can be used to reduce bias.

What pre-trained machine learning APIS would you use in this image processing pipeline?

Some pre-trained machine learning APIs that can be used in an image processing pipeline include Google Cloud Vision API, Microsoft Azure Computer Vision API, and Amazon Rekognition API. These APIs can be used to extract features from images, classify images, detect objects, and perform facial recognition, among other tasks.

Which machine learning API is used to convert audio to text in GCP?

The machine learning API used to convert audio to text in GCP is the Cloud Speech-to-Text API. This API can be used to transcribe audio files, recognize spoken words, and convert spoken language into text in real-time. The API uses machine learning models to analyze the audio and generate accurate transcriptions.

How can machine learning reduce bias and variance?

Machine learning can reduce bias and variance by using different techniques, such as regularization, cross-validation, and ensemble learning. Regularization can help reduce variance by adding a penalty term to the cost function, which prevents overfitting. Cross-validation can help reduce bias by using different subsets of the data to train and test the model. Ensemble learning can also help reduce bias and variance by combining multiple models to make more accurate predictions.

How does machine learning increase precision?

Machine learning can increase precision by optimizing the model for accuracy. This can be achieved by using techniques such as feature selection, hyperparameter tuning, and regularization. Feature selection helps to identify the most important features in the dataset, which can improve the model’s precision. Hyperparameter tuning involves adjusting the settings of the model to find the optimal combination that leads to the best performance. Regularization helps to reduce overfitting and improve the model’s generalization ability.

How to do research in machine learning?

To do research in machine learning, one should start by identifying a research problem or question. Then, they can review relevant literature to understand the state-of-the-art techniques and approaches. Once the problem has been defined and the relevant literature has been reviewed, the researcher can collect and preprocess the data, design and implement the model, and evaluate the results. It is also important to document the research and share the findings with the community.

Is associations a machine learning technique?

Associations can be considered a machine learning technique, specifically in the field of unsupervised learning. Association rules mining is a popular technique used to discover interesting relationships between variables in a dataset. It is often used in market basket analysis to find correlations between items purchased together by customers. However, it is important to note that associations are not typically considered a supervised learning technique, as they do not involve predicting a target variable.

How do you present a machine learning model?

To present a machine learning model, it is important to provide a clear explanation of the problem being addressed, the dataset used, and the approach taken to build the model. The presentation should also include a description of the model architecture and any preprocessing techniques used. It is also important to provide an evaluation of the model’s performance using relevant metrics, such as accuracy, precision, and recall. Finally, the presentation should include a discussion of the model’s limitations and potential areas for improvement.

Is moving average machine learning?

Moving average is a statistical method used to analyze time series data, and it is not typically considered a machine learning technique. However, moving averages can be used as a preprocessing step for machine learning models to smooth out the data and reduce noise. In this context, moving averages can be considered a feature engineering technique that can improve the performance of the model.

How do you calculate accuracy and precision in machine learning?

Accuracy and precision are common metrics used to evaluate the performance of machine learning models. Accuracy is the proportion of correct predictions made by the model, while precision is the proportion of correct positive predictions out of all positive predictions made. To calculate accuracy, divide the number of correct predictions by the total number of predictions made. To calculate precision, divide the number of true positives (correct positive predictions) by the total number of positive predictions made by the model.

Which stage of the machine learning workflow includes feature engineering?

The stage of the machine learning workflow that includes feature engineering is the “data preparation” stage, where the data is cleaned, preprocessed, and transformed in a way that prepares it for training and testing the machine learning model. Feature engineering is the process of selecting, extracting, and transforming the most relevant and informative features from the raw data to be used by the machine learning algorithm.

How do I make machine learning AI?

Artificial Intelligence (AI) is a broader concept that includes several subfields, such as machine learning, natural language processing, and computer vision. To make a machine learning AI system, you will need to follow a systematic approach, which involves the following steps:

  1. Define the problem and collect relevant data.
  2. Preprocess and transform the data for training and testing.
  3. Select and train a suitable machine learning model.
  4. Evaluate the performance of the model and fine-tune it.
  5. Deploy the model and integrate it into the target system.

How do you select models in machine learning?

The process of selecting a suitable machine learning model involves the following steps:

  1. Define the problem and the type of prediction required.
  2. Determine the type of data available (structured, unstructured, labeled, or unlabeled).
  3. Select a set of candidate models that are suitable for the problem and data type.
  4. Evaluate the performance of each model using a suitable metric (e.g., accuracy, precision, recall, F1 score).
  5. Select the best performing model and fine-tune its parameters and hyperparameters.

What is convolutional neural network in machine learning?

A Convolutional Neural Network (CNN) is a type of deep learning neural network that is commonly used in computer vision applications, such as image recognition, classification, and segmentation. It is designed to automatically learn and extract hierarchical features from the raw input image data using convolutional layers, pooling layers, and fully connected layers.

The convolutional layers apply a set of learnable filters to the input image, which help to extract low-level features such as edges, corners, and textures. The pooling layers downsample the feature maps to reduce the dimensionality of the data and increase the computational efficiency. The fully connected layers perform the classification or regression task based on the learned features.

How to use machine learning in Excel?

Excel provides several built-in machine learning tools and functions that can be used to perform basic predictive analysis on structured data, such as linear regression, logistic regression, decision trees, and clustering. To use machine learning in Excel, you can follow these general steps:

  1. Organize your data in a structured format, with each row representing a sample and each column representing a feature or target variable.
  2. Use the appropriate machine learning function or tool to build a predictive model based on the data.
  3. Evaluate the performance of the model using appropriate metrics and test data.

What are the six distinct stages or steps that are critical in building successful machine learning based solutions?

The six distinct stages or steps that are critical in building successful machine learning based solutions are:

  • Problem definition
  • Data collection and preparation
  • Feature engineering
  • Model training
  • Model evaluation
  • Model deployment and monitoring

Which two actions should you consider when creating the azure machine learning workspace?

When creating the Azure Machine Learning workspace, two important actions to consider are:

  • Choosing an appropriate subscription that suits your needs and budget.
  • Deciding on the region where you want to create the workspace, as this can impact the latency and data transfer costs.

What are the three stages of building a model in machine learning?

The three stages of building a model in machine learning are:

  • Model building
  • Model evaluation
  • Model deployment

How to scale a machine learning system?

Some ways to scale a machine learning system are:

  • Using distributed training to leverage multiple machines for model training
  • Optimizing the code to run more efficiently
  • Using auto-scaling to automatically add or remove computing resources based on demand

Where can I get machine learning data?

Machine learning data can be obtained from various sources, including:

  • Publicly available datasets such as UCI Machine Learning Repository and Kaggle
  • Online services that provide access to large amounts of data such as AWS Open Data and Google Public Data
  • Creating your own datasets by collecting data through web scraping, surveys, and sensors

How do you do machine learning research?

To do machine learning research, you typically:

  • Identify a research problem or question
  • Review relevant literature to understand the state-of-the-art and identify research gaps
  • Collect and preprocess data
  • Design and implement experiments to test hypotheses or evaluate models
  • Analyze the results and draw conclusions
  • Document the research in a paper or report

How do you write a machine learning project on a resume?

To write a machine learning project on a resume, you can follow these steps:

  • Start with a brief summary of the project and its goals
  • Describe the datasets used and any preprocessing done
  • Explain the machine learning techniques used, including any specific algorithms or models
  • Highlight the results and performance metrics achieved
  • Discuss any challenges or limitations encountered and how they were addressed
  • Showcase any additional skills or technologies used such as data visualization or cloud computing

What are two ways that marketers can benefit from machine learning?

Marketers can benefit from machine learning in various ways, including:

  • Personalized advertising: Machine learning can analyze large volumes of data to provide insights into the preferences and behavior of individual customers, allowing marketers to deliver personalized ads to specific audiences.
  • Predictive modeling: Machine learning algorithms can predict consumer behavior and identify potential opportunities, enabling marketers to optimize their marketing strategies for better results.

How does machine learning remove bias?

Machine learning can remove bias by using various techniques, such as:

  • Data augmentation: By augmenting data with additional samples or by modifying existing samples, machine learning models can be trained on more diverse data, reducing the potential for bias.
  • Fairness constraints: By setting constraints on the model’s output to ensure that it meets specific fairness criteria, machine learning models can be designed to reduce bias in decision-making.
  • Unbiased training data: By ensuring that the training data is unbiased, machine learning models can be designed to reduce bias in decision-making.

Is structural equation modeling machine learning?

Structural equation modeling (SEM) is a statistical method used to test complex relationships between variables. While SEM involves the use of statistical models, it is not considered to be a machine learning technique. Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.

How do you predict using machine learning?

To make predictions using machine learning, you typically need to follow these steps:

  • Collect and preprocess data: Collect data that is relevant to the prediction task and preprocess it to ensure that it is in a suitable format for machine learning.
  • Train a model: Use the preprocessed data to train a machine learning model that is appropriate for the prediction task.
  • Test the model: Evaluate the performance of the model on a test set of data that was not used in the training process.
  • Make predictions: Once the model has been trained and tested, it can be used to make predictions on new, unseen data.

Does Machine Learning eliminate bias?

No, machine learning does not necessarily eliminate bias. While machine learning can be used to detect and mitigate bias in some cases, it can also perpetuate or even amplify bias if the data used to train the model is biased or if the algorithm is not designed to address potential sources of bias.

Is clustering a machine learning algorithm?

Yes, clustering is a machine learning algorithm. Clustering is a type of unsupervised learning that involves grouping similar data points together into clusters based on their similarities. Clustering algorithms can be used for a variety of tasks, such as identifying patterns in data, segmenting customer groups, or organizing search results.

Is machine learning data analysis?

Machine learning can be used as a tool for data analysis, but it is not the same as data analysis. Machine learning involves using algorithms to learn patterns in data and make predictions based on that learning, while data analysis involves using various techniques to analyze and interpret data to extract insights and knowledge.

How do you treat categorical variables in machine learning?

Categorical variables can be represented numerically using techniques such as one-hot encoding, label encoding, and binary encoding. One-hot encoding involves creating a binary variable for each category, label encoding involves assigning a unique integer value to each category, and binary encoding involves converting each category to a binary code. The choice of technique depends on the specific problem and the type of algorithm being used.

How do you deal with skewed data in machine learning?

Skewed data can be addressed in several ways, depending on the specific problem and the type of algorithm being used. Some techniques include transforming the data (e.g., using a logarithmic or square root transformation), using weighted or stratified sampling, or using algorithms that are robust to skewed data (e.g., decision trees, random forests, or support vector machines).

How do I create a machine learning application?

Creating a machine learning application involves several steps, including identifying a problem to be solved, collecting and preparing the data, selecting an appropriate algorithm, training the model on the data, evaluating the performance of the model, and deploying the model to a production environment. The specific steps and tools used depend on the problem and the technology stack being used.

Is heuristics a machine learning technique?

Heuristics is not a machine learning technique. Heuristics are general problem-solving strategies that are used to find solutions to problems that are difficult or impossible to solve using formal methods. In contrast, machine learning involves using algorithms to learn patterns in data and make predictions based on that learning.

Is Bayesian statistics machine learning?

Bayesian statistics is a branch of statistics that involves using Bayes’ theorem to update probabilities as new information becomes available. While machine learning can make use of Bayesian methods, Bayesian statistics is not itself a machine learning technique.

Is Arima machine learning?

ARIMA (autoregressive integrated moving average) is a statistical method used for time series forecasting. While it is sometimes used in machine learning applications, ARIMA is not itself a machine learning technique.

Can machine learning solve all problems?

No, machine learning cannot solve all problems. Machine learning is a tool that is best suited for solving problems that involve large amounts of data and complex patterns.

Some problems may not have enough data to learn from, while others may be too simple to require the use of machine learning. Additionally, machine learning algorithms can be biased or overfitted, leading to incorrect predictions or recommendations.

What are parameters and hyperparameters in machine learning?

In machine learning, parameters are the values that are learned by the algorithm during training to make predictions. Hyperparameters, on the other hand, are set by the user and control the behavior of the algorithm, such as the learning rate, number of hidden layers, or regularization strength.

What are two ways that a marketer can provide good data to a Google app campaign powered by machine learning?

Two ways that a marketer can provide good data to a Google app campaign powered by machine learning are by providing high-quality creative assets, such as images and videos, and by setting clear conversion goals that can be tracked and optimized.

Is Tesseract a machine learning?

Tesseract is an optical character recognition (OCR) engine that uses machine learning algorithms to recognize text in images. While Tesseract uses machine learning, it is not a general-purpose machine learning framework or library.

How do you implement a machine learning paper?

Implementing a machine learning paper involves first understanding the problem being addressed and the approach taken by the authors. The next step is to implement the algorithm or model described in the paper, which may involve writing code from scratch or using existing libraries or frameworks. Finally, the implementation should be tested and evaluated using appropriate metrics and compared to the results reported in the paper.

What is mean subtraction in machine learning?

Mean subtraction is a preprocessing step in machine learning that involves subtracting the mean of a dataset or a batch of data from each data point. This can help to center the data around zero and remove bias, which can improve the performance of some algorithms, such as neural networks.

What are the first two steps of a typical machine learning workflow?

The first two steps of a typical machine learning workflow are data collection and preprocessing. Data collection involves gathering data from various sources and ensuring that it is in a usable format.

Preprocessing involves cleaning and preparing the data, such as removing duplicates, handling missing values, and transforming categorical variables into a numerical format. These steps are critical to ensure that the data is of high quality and can be used to train and evaluate machine learning models.

What are The applications and challenges of natural language processing (NLP), the field of artificial intelligence that deals with human language?

Natural language processing (NLP) is a field of artificial intelligence that deals with the interactions between computers and human language. NLP has numerous applications in various fields, including language translation, information retrieval, sentiment analysis, chatbots, speech recognition, and text-to-speech synthesis.

Applications of NLP:

  1. Language Translation: NLP enables computers to translate text from one language to another, providing a valuable tool for cross-cultural communication.

  2. Information Retrieval: NLP helps computers understand the meaning of text, which facilitates searching for specific information in large datasets.

  3. Sentiment Analysis: NLP allows computers to understand the emotional tone of a text, enabling businesses to measure customer satisfaction and public sentiment.

  4. Chatbots: NLP is used in chatbots to enable computers to understand and respond to user queries in natural language.

  5. Speech Recognition: NLP is used to convert spoken language into text, which can be useful in a variety of settings, such as transcription and voice-controlled devices.

  6. Text-to-Speech Synthesis: NLP enables computers to convert text into spoken language, which is useful in applications such as audiobooks, voice assistants, and accessibility software.

Challenges of NLP:

  1. Ambiguity: Human language is often ambiguous, and the same word or phrase can have multiple meanings depending on the context. Resolving this ambiguity is a significant challenge in NLP.

  2. Cultural and Linguistic Diversity: Languages vary significantly across cultures and regions, and developing NLP models that can handle this diversity is a significant challenge.

  3. Data Availability: NLP models require large amounts of training data to perform effectively. However, data availability can be a challenge, particularly for languages with limited resources.

  4. Domain-specific Language: NLP models may perform poorly when confronted with domain-specific language, such as jargon or technical terms, which are not part of their training data.

  5. Bias: NLP models can exhibit bias, particularly when trained on biased datasets or in the absence of diverse training data. Addressing this bias is critical to ensuring fairness and equity in NLP applications.

Artificial Intelligence Frequently Asked Questions – Conclusion:

AI is an increasingly hot topic in the tech world, so it’s only natural that curious minds may have some questions about what AI is and how it works. From AI fundamentals to machine learning, data science, and beyond, we hope this collection of AI Frequently Asked Questions have you covered and can help you become one step closer to AI mastery!

AI Unraveled

 

 

Ai Unraveled Audiobook at Google Play: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM

How AI is Impacting Smartphone Longevity – Best Smartphones 2023

It is a highly recommended read for those involved in the future of education and especially for those in the professional groups mentioned in the paper. The authors predict that AI will have an impact on up to 80% of all future jobs. Meaning this is one of the most important topics of our time, and that is crucial that we prepare for it.

According to the paper, certain jobs are particularly vulnerable to AI, with the following jobs being considered 100% exposed:

👉Mathematicians

👉Tax preparers

👉Financial quantitative analysts

👉Writers and authors

👉Web and digital interface designers

👉Accountants and auditors

👉News analysts, reporters, and journalists

👉Legal secretaries and administrative assistants

👉Clinical data managers

👉Climate change policy analysts

There are also a number of jobs that were found to have over 90% exposure, including correspondence clerks, blockchain engineers, court reporters and simultaneous captioners, and proofreaders and copy markers.

The team behind the paper (Tyna Eloundou, Sam Manning, Pamela Mishkin & Daniel Rock) concludes that most occupations will be impacted by AI to some extent.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

#education #research #jobs #future #futureofwork #ai

By Bill Gates

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions

In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.

The first time was in 1980, when I was introduced to a graphical user interface—the forerunner of every modern operating system, including Windows. I sat with the person who had shown me the demo, a brilliant programmer named Charles Simonyi, and we immediately started brainstorming about all the things we could do with such a user-friendly approach to computing. Charles eventually joined Microsoft, Windows became the backbone of Microsoft, and the thinking we did after that demo helped set the company’s agenda for the next 15 years.

The second big surprise came just last year. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts—it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough.

I thought the challenge would keep them busy for two or three years. They finished it in just a few months.

In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score, and the equivalent to getting an A or A+ in a college-level biology course.

Once it had aced the test, we asked it a non-scientific question: “What do you say to a father with a sick child?” It wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning.

I knew I had just seen the most important advance in technology since the graphical user interface.

This inspired me to think about all the things that AI can achieve in the next five to 10 years.

The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.

Philanthropy is my full-time job these days, and I’ve been thinking a lot about how—in addition to helping people be more productive—AI can reduce some of the world’s worst inequities. Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children.

I’ve been thinking a lot about how AI can reduce some of the world’s worst inequities.

In the United States, the best opportunity for reducing inequity is to improve education, particularly making sure that students succeed at math. The evidence shows that having basic math skills sets students up for success, no matter what career they choose. But achievement in math is going down across the country, especially for Black, Latino, and low-income students. AI can help turn that trend around.

Climate change is another issue where I’m convinced AI can make the world more equitable. The injustice of climate change is that the people who are suffering the most—the world’s poorest—are also the ones who did the least to contribute to the problem. I’m still thinking and learning about how AI can help, but later in this post I’ll suggest a few areas with a lot of potential.

Impact that AI will have on issues that the Gates Foundation  works on

In short, I’m excited about the impact that AI will have on issues that the Gates Foundation  works on, and the foundation will have much more to say about AI in the coming months. The world needs to make sure that everyone—and not just people who are well-off—benefits from artificial intelligence. Governments and philanthropy will need to play a major role in ensuring that it reduces inequity and doesn’t contribute to it. This is the priority for my own work related to AI.

Any new technology that’s so disruptive is bound to make people uneasy, and that’s certainly true with artificial intelligence. I understand why—it raises hard questions about the workforce, the legal system, privacy, bias, and more. AIs also make factual mistakes and experience hallucinations. Before I suggest some ways to mitigate the risks, I’ll define what I mean by AI, and I’ll go into more detail about some of the ways in which it will help empower people at work, save lives, and improve education.

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions- The Age of AI has begun

Defining artificial intelligence

Technically, the term artificial intelligencerefers to a model created to solve a specific problem or provide a particular service. What is powering things like ChatGPT is artificial intelligence. It is learning how to do chat better but can’t learn other tasks. By contrast, the term artificial general intelligence refers to software that’s capable of learning any task or subject. AGI doesn’t exist yet—there is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all.

Developing AI and AGI has been the great dream of the computing industry

Developing AI and AGI has been the great dream of the computing industry. For decades, the question was when computers would be better than humans at something other than making calculations. Now, with the arrival of machine learning and large amounts of computing power, sophisticated AIs are a reality and they will get better very fast.

I think back to the early days of the personal computing revolution, when the software industry was so small that most of us could fit onstage at a conference. Today it is a global industry. Since a huge portion of it is now turning its attention to AI, the innovations are going to come much faster than what we experienced after the microprocessor breakthrough. Soon the pre-AI period will seem as distant as the days when using a computer meant typing at a C:> prompt rather than tapping on a screen.

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions –

Productivity enhancement

Although humans are still better than GPT at a lot of things, there are many jobs where these capabilities are not used much. For example, many of the tasks done by a person in sales (digital or phone), service, or document handling (like payables, accounting, or insurance claim disputes) require decision-making but not the ability to learn continuously. Corporations have training programs for these activities and in most cases, they have a lot of examples of good and bad work. Humans are trained using these data sets, and soon these data sets will also be used to train the AIs that will empower people to do this work more efficiently.

As computing power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks. Microsoft describes this as having a co-pilot. Fully incorporated into products like Office, AI will enhance your work—for example by helping with writing emails and managing your inbox.

Eventually your main way of controlling a computer will no longer be pointing and clicking or tapping on menus and dialogue boxes. Instead, you’ll be able to write a request in plain English. (And not just English—AIs will understand languages from around the world. In India earlier this year, I met with developers who are working on AIs that will understand many of the languages spoken there.)

In addition, advances in AI will enable the creation of a personal agent. Think of it as a digital personal assistant: It will see your latest emails, know about the meetings you attend, read what you read, and read the things you don’t want to bother with. This will both improve your work on the tasks you want to do and free you from the ones you don’t want to do.

Advances in AI will enable the creation of a personal agent.

You’ll be able to use natural language to have this agent help you with scheduling, communications, and e-commerce, and it will work across all your devices. Because of the cost of training the models and running the computations, creating a personal agent is not feasible yet, but thanks to the recent advances in AI, it is now a realistic goal. Some issues will need to be worked out: For example, can an insurance company ask your agent things about you without your permission? If so, how many people will choose not to use it?

 

Ai Unraveled Audiobook at Google Play: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM

How AI is Impacting Smartphone Longevity – Best Smartphones 2023

 

 

 
 
 

 

 

Advanced Guide to Interacting with ChatGPT

Artificial Intelligence Gateway The goal of the r/ArtificialIntelligence is to provide a gateway to the many different facets of the Artificial Intelligence community, and to promote discussion relating to the ideas and concepts that we know of as AI. These could include philosophical and social questions, art and design, technical papers, machine learning, where to find resources and tools, how to develop AI/ML projects, AI in business, how AI is affecting our lives, what the future may hold, and many other topics. Welcome.

  • The survival algorithm.
    by /u/Cervantes6785 on April 23, 2024 at 6:56 am

    submitted by /u/Cervantes6785 [link] [comments]

  • What is Lambda billing?
    by /u/krunal_bhimani_ on April 23, 2024 at 6:42 am

    Lambda billing is based on the number of computing resources used by your functions. AWS bills you on two parameters: Number of requests made to your function Duration of each request This means that you are charged for the time it takes for your function to execute, as well as the number of times your function is invoked. In addition to these charges, AWS also charges for data transfer in and out of your Lambda function. This includes data transfer over the internet and between AWS services. If your function uses other AWS services, such as Amazon S3 or Amazon DynamoDB, you may incur additional charges for these services. https://www.seaflux.tech/blogs/aws-lambda-pricing-explained submitted by /u/krunal_bhimani_ [link] [comments]

  • How Generative AI is Transforming Industries
    by /u/krunal_bhimani_ on April 23, 2024 at 6:07 am

    Generative AI is changing the game across industries, disrupting traditional processes and creating new opportunities for innovation. Here are some generative ai examples that are transforming industries: Art and Design: With the help of generative AI, artists, and designers can unleash their creativity to create stunning, never-before-seen masterpieces. AI-generated artworks are taking the art world by storm, while designers are using AI to produce novel designs that would have been impossible otherwise. Healthcare: Generative AI is taking healthcare to the next level by providing personalized treatment plans based on a patient's medical history and current condition. By analyzing vast amounts of data, AI systems can identify the most effective treatment methods, resulting in better patient outcomes. Finance: Generative AI is revolutionizing the financial industry by analyzing vast amounts of data to detect patterns and predict market trends. AI-generated investment recommendations are helping investors make more informed decisions, while automated trading systems are transforming the way trades are executed. Film and Entertainment: Generative AI is transforming the film and entertainment industry by creating realistic virtual characters and environments. By using AI to generate 3D models and animations, filmmakers and game developers can create highly immersive and realistic experiences for their audiences. https://www.seaflux.tech/blogs/generative-ai-industry-scope submitted by /u/krunal_bhimani_ [link] [comments]

  • Text-To-Speech with Tone and Mannerism, How do I do this?
    by /u/Skylight_Chaser on April 23, 2024 at 5:58 am

    Hi there! I'm curious to make a voice assistant that has a bit of sarcasm in their voice. A bit of wit, a bit of like, playful banter. Almost like she has this playful tone with you. Why? Because it's damn weird when I use my current text-to-speech and it sounds like she's held at gunpoint to say the words. I want to have fun with it! So I was wondering if anyone found a text-to-speech software that can help convey tone and speech patterns. Right now I'm using Elevenlabs to create realistic synthetic voices but I would love something along the lines of like that AI assistant from Across the Spider-Verse or the voice & mannerism from the handler in bullet train. Is this possible or do I have to train my own models? submitted by /u/Skylight_Chaser [link] [comments]

  • One-Minute Daily AI News 4/22/2024
    by /u/Excellent-Target-847 on April 23, 2024 at 4:33 am

    Tech’s earnings bonanza this week shines spotlight on growing troubles at Tesla, Google.[1] Paedophiles are being urged to use artificial intelligence to create nude images of children to extort more extreme material from them, according to a child abuse charity.[2] Google DeepMind RecurrentGemma Beats Transformer Models.[3] AMD teams up with Arm to unveil AI chip family that does preprocessing, inference and postprocessing on one silicon.[4] Sources included at: https://bushaicave.com/2024/04/22/4-22-2024/ submitted by /u/Excellent-Target-847 [link] [comments]

  • Metadata analysis to dynamically adjust tokens within a context view of a LLM?
    by /u/IWantAGI on April 23, 2024 at 4:00 am

    I have this sort of halfway concept that I'm attempting to refine and hopefully build out... But am not 100% sure if this even makes sense or is worth attempting. LLMs establish relationships of tokens based on the association of tokens within the training data. They then respond to input based on those relationships. By using large models and/or large compute you can use a fairly large context window which is good for a lot of things; however, the context window is a limiting factor. There are a few methods that attempt to effectively expand the context window, things like sparse attention, but (and over simplifying/ probably not explaining it 100% correctly) these work by reducing focus to highly related tokens. Anyways, my idea is that if you can train a small model on the relationship of groups of tokens, e.g. how one code block relates to another, without focusing on what individual code blocks do, it should be possible to use this as a sort of filter for the context window. The concept here would be that you could feed the AI a codebase larger than the context window, ask it a question about the code, and through the "filter" could reduce the code blocks, based on their relationship to each other, until it fits within the context window of the "main" LLM. It would add some overhead, but being an instance based filter, should be relatively small, maybe 1-2B. Presuming this could work (or if it even makes any sense) it could be combined with a smaller model, say 7B, to significantly improve it's capabilities. I'm specifically looking at smaller models because they can be used relatively easily on mid-grade consumer hardware (e.g. CPU with 16 gigs of RAM), allowing for improved local AI to be incorporated into various software stacks. (Note, I'm just a hobbiest and and haven't gone beyond trying to train a model with a few million parameters while experimenting & learning) submitted by /u/IWantAGI [link] [comments]

  • The AI Weekly Report celebrates its one year anniversary!
    by /u/redmage123 on April 23, 2024 at 2:54 am

    Hello all, The AI Weekly report is celebrating the one year aniversary of its inception, check out our articles, technical reports and primers over at https://weeklyreport.ai It's completely free and doesn't even require a sign up. If you do sign up, however, we will send you our weekly news digest of the 15 top AI stories directly to your chosen e-mail inbox. We hope to see you there! submitted by /u/redmage123 [link] [comments]

  • Looking for some guidance
    by /u/Low_Possession3617 on April 23, 2024 at 1:53 am

    I am semi new to AI I have been watching all these amazing new site/uses for ai and I want to see if I can find a way to be involved I would love to learn but don’t really know where to start, I have been fiddling around some image/video generators and I see people with no coding background making apps I’m hoping to start learning how to do prompt engineering or something substantial there’s just so much going on in the space I don’t know where to get in any advice? Sorry if I’m in the wrong tags or whatever submitted by /u/Low_Possession3617 [link] [comments]

  • Music generating AI?
    by /u/nathankrebs on April 23, 2024 at 1:12 am

    I'm familiar with the vast array of text-to-music services like Suno and etc but I'm interested in finding something that I can really fine tune my preferences with. I'm not sure what I want even exists quite frankly, but figured reddit is the place to come to find out. I want to find a generative AI software that I can run locally on my Windows desktop (or Linux if necessary) and train on my own data set of audio files. I want to download a shit ton of my favorite songs and then train my own AI model on it to create new stuff. Options? If any? submitted by /u/nathankrebs [link] [comments]

  • Help required with prompt development/tool selection
    by /u/LivingOkInTheBay on April 23, 2024 at 1:03 am

    So I've been trying to use various voice ai tools to generate a rendition of the Beach Boys' Barbara Ann but performed by JFK, RFK, and Ted Kennedy. The results are catastrophically hilarious (see comments), but I'm committed to seeing this idea through. Any and all advice is welcome! Link to my results this far: https://www.youtube.com/watch?v=-wLFlvFnEeE submitted by /u/LivingOkInTheBay [link] [comments]

  • Concealed Conversations.
    by /u/Cervantes6785 on April 23, 2024 at 12:34 am

    submitted by /u/Cervantes6785 [link] [comments]

  • Over 300 new job listings weekly on AI-Powered Job Board
    by /u/lolaks181203 on April 22, 2024 at 11:43 pm

    Hi everyone! It's been an exciting week since I launched a job board designed to help you find your dream job thanks to AI-powered smart filters and skills matching. Currently, +300 new job opportunities are added each week, and +650 users signed up and are already taking advantage of the platform. Also, now you can become an affiliate and earn 30% recurring on all paid referrals. I'd love to hear your feedback to continue improving your experience. https://skillexchange.xyz/ submitted by /u/lolaks181203 [link] [comments]

  • who is making more optimistic, informed, predictions about ai than vinod khosla? here are ten they would have to meet or top
    by /u/Georgeo57 on April 22, 2024 at 11:32 pm

    AI will make expertise like doctors, lawyers, and tutors nearly free and accessible to everyone 24/7. There will be over a billion bipedal robots in 25 years (and a million in 10 years), freeing humans from undesirable jobs and creating an industry larger than today's auto industry. The number of programmers will exceed a billion, as they'll be able to program in human language, dramatically expanding the scope of computers. AI will personalize entertainment, increase content diversity and creativity, and enable many more creators. Most consumer internet access will be through AI agents acting on behalf of users. Medicine will shift towards precision care based on patients' genomics and AI models of each individual. Alternative proteins will replace traditional animal protein, and "green" fertilizers will be developed. Autonomous vehicles could replace most cars in cities. Mach 5 planes running on sustainable fuel will make global travel much faster. By 2050, fusion and advanced geothermal will provide clean, dispatchable electric power, replacing fossil fuels. submitted by /u/Georgeo57 [link] [comments]

  • How to train image models to generate tattoos?
    by /u/14392 on April 22, 2024 at 10:05 pm

    I'm not aure about how to go about training a model to generate bw and colors tattoos, I can find several examples online, and what model you suggest is good for the job. I can remove backgrounds using a service api that allows that. Thanks submitted by /u/14392 [link] [comments]

  • Has anyone created a Halloween inspired AI that takes a live video of a person and adds horror highlights such as blood, fangs, skeleton etc...?
    by /u/Ikickyouinthebrains on April 22, 2024 at 8:30 pm

    Would prefer free software, but am willing to pay a small fee to use this. Would like to get this ready for this Halloween. submitted by /u/Ikickyouinthebrains [link] [comments]

  • Has anyone created a Halloween inspired AI that takes a live video of a person and adds horror highlights such as blood, fangs, skeleton etc...?
    by /u/Ikickyouinthebrains on April 22, 2024 at 8:30 pm

    Would prefer free software, but am willing to pay a small fee to use this. Would like to get this ready for this Halloween. submitted by /u/Ikickyouinthebrains [link] [comments]

  • Speech to speech AI accent converter?
    by /u/AyLilDoo on April 22, 2024 at 8:28 pm

    Looking for website or app that can convert my uploaded audio file of my speech, to generated speech in another accent, i.e. US English to UK English. After lots of searching and experimenting, I found Respeecher but the results were meh. submitted by /u/AyLilDoo [link] [comments]

  • Could we develop an AI under the GPT model that fully pretends to be conscious?
    by /u/Feeling_Luck_2039 on April 22, 2024 at 8:21 pm

    This would basically be what “conscious” ai would be like albeit its not actually conscious but with the way chat gpt responds couldn’ they just code it to pretend to receptively question the world and give humanlike responses some sarcastic or funny etc. If so why the hell havent they developed that yet. submitted by /u/Feeling_Luck_2039 [link] [comments]

  • Agi predictions doesn't make sense
    by /u/Ashamed-Ordinary8543 on April 22, 2024 at 8:10 pm

    We often hear statements like ‘AGI within this year’ or ‘AGI within this decade,’ but have we ever truly questioned the reason behind these analyses? Today, I propose a new perspective on this topic and introduce a fresh way of thinking. When discussions about the ‘probable advent of AGI’ take place, they presuppose and assume that AGI will eventually happen. Each of us who reads and engages in these discussions unconsciously accepts that AGI will exist in the future. However, personally, when I hear such declarations from the AI community, I wonder about their true purpose. What do these analyses really serve? The Average Person’s Reaction: The average person might be surprised (if the prediction is short-term) or completely disinterested (if it’s long-term). Regardless, most people are not truly engaged in discussing AGI beyond a certain limit—they don’t actively prepare for it. Why Prepare for AGI?: It is essential to prepare for AGI for two reasons: Adapting to Basic Human Tasks: We need to find new jobs that exclude those at risk, ensuring that AGI doesn’t catch us off guard unexpectedly. Exploring New Thought Models: The research process I described helps us discover new ways of thinking, allowing us to pave paths toward a more authentic and human-centric future. (Assumption: AGI will struggle with creative tasks; let’s find ways for humans to excel in those areas!) submitted by /u/Ashamed-Ordinary8543 [link] [comments]

  • AI site similar to Gemini that let's you save responses as a PDF
    by /u/Zolo89 on April 22, 2024 at 7:17 pm

    Hi, I've been using Gemini to paraphrase notes for studying and want to know if there's a similar site that lets you save the responses as a PDF. The reason I'm asking is that Gemini no longer lets me print to / save the responses as a PDF. Thanks. submitted by /u/Zolo89 [link] [comments]

Machine Learning 101 – Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps

AWS machine Learning Specialty Exam Prep MLS-C01

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

What are the Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps?

This blog is the best way  is the best way to prepare for your upcoming  AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar  that are very similar to the real exam. It also includes  the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

The typical Google Machine Learning Engineer salary is $147,218. Machine Learning Engineer salaries at Google can range from $110,000 – $152,183.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

  • By the end of 2020, 85% of customer interactions will be handled without a human (Call Center, Chatbot, etc…)
  • 61% of marketers say artificial intelligence is the most important aspect of their data strategy.
  • 80% of business and tech leaders say AI already boosts productivity (Robotic Process Automation, Power Automate, etc..)
  • Current AI technology can boost business productivity by up to 40%

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AWS Certified machine Learning Specialty Exam Prep MLS-C01 - Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps
AWS machine Learning Specialty Exam Prep MLS-C01

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GCP Professional Machine Learning Engineer

 

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What does a Professional Machine Learning Engineer do?

Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.


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The AWS Certified Machine Learning – Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

This blog covers Machine Learning 101, Top 20 AWS Certified Machine Learning Specialty Questions and Answers, Top 20 Google Professional Machine Learning Engineer Sample Questions, Machine Learning Quizzes, Machine Learning Q&A, Top 10 Machine Learning Algorithms, Machine Learning Latest Hot News, Machine Learning Demos (Ex: Tensorflow Demos)

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

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D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

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Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 
A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?
A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 
Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 
Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 
A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 

 
Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?
A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 
Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 
 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 
Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 
A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 
Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21:

Answer21:

What are the Top 100 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps?

This blog is the best way  is the best way to prepare for your upcoming  AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar  that are very similar to the real exam. It also includes  the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.

The typical Google Machine Learning Engineer salary is $147,218. Machine Learning Engineer salaries at Google can range from $110,000 – $152,183.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

  • By the end of 2020, 85% of customer interactions will be handled without a human (Call Center, Chatbot, etc…)
  • 61% of marketers say artificial intelligence is the most important aspect of their data strategy.
  • 80% of business and tech leaders say AI already boosts productivity (Robotic Process Automation, Power Automate, etc..)
  • Current AI technology can boost business productivity by up to 40%

AWS Machine Learning Certification Specialty Exam Prep for iOs Android Windows10/11

AWS machine Learning Specialty Exam Prep MLS-C01 - Top 200 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps
AWS machine Learning Specialty Exam Prep MLS-C01

GCP Professional Machine Learning Engineer for iOs, Android, Windows 10/11

Quizzes, Practice Exams: Framing, Architecting, Designing, Developing ML Problems & Solutions, ML Jobs Interview Q&A

GCP Professional Machine Learning Engineer
GCP Professional Machine Learning Engineer

 

Azure AI Fundamentals AI-900 Exam Prep App for iOS, Android, Windows10/11

Basics and Advanced Machine Learning Quizzes on Azure, Azure Machine Learning Job Interviews Questions and Answer, ML Cheat Sheets

Azure AI Fundamentals AI-900 Exam Prep
Azure AI Fundamentals AI-900 Exam Prep

Machine Learning For Dummies App for iOs, Android, Windows10/11

Use this App to learn about Machine Learning and Elevate your Brain with Machine Learning Quizzes, Cheat Sheets, Ml Jobs Interview Questions and Answers updated daily.

Machine Learning For Dummies
Machine Learning For Dummies

What does a Professional Machine Learning Engineer do?

Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.

The AWS Certified Machine Learning – Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

This blog covers Machine Learning 101, Top 20 AWS Certified Machine Learning Specialty Questions and Answers, Top 20 Google Professional Machine Learning Engineer Sample Questions, Machine Learning Quizzes, Machine Learning Q&A, Top 10 Machine Learning Algorithms, Machine Learning Latest Hot News, Machine Learning Demos (Ex: Tensorflow Demos)

Below are the Top 100 AWS Certified Machine Learning Specialty Questions and Answers Dumps.

Top

 

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 
A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?
A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 
Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 
Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 
A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 
 
Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?
A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 
Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 
 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 
Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 
A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 
Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21

Answer21:

 

Notes 21: 

Question22

Answer22:

 

Notes 22: 

Question23

Answer23:

 

Notes 23: 

Question24

Answer24:

 

Notes 24: 

What are the Top 100 AWS and Google Certified Machine Learning Specialty Questions and Answers Dumps?

This blog is the best way  is the best way to prepare for your upcoming  AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar  that are very similar to the real exam. It also includes  the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.

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Below are the Top 100 AWS Certified Machine Learning Specialty Questions and Answers Dumps.

Top

 

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 
A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?
A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 
Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 
Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 
A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 
 
Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?
A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 
Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 
 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 
Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 
A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 
Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21:

Answer21:

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This blog is the best way  is the best way to prepare for your upcoming  AWS Certified Machine Learning Specialty and Google Certified Professional Machine Learning Engineer exam. With over 100 questions and answers, this blog provides quizzes similar  that are very similar to the real exam. It also includes  the option to show and hide answers. Additionally, there are machine learning interview questions and detailed answers, as well as cheat sheets and illustrations. This blog is the best way to make sure you are well-prepared for your AWS Certified Machine Learning Specialty Exam.

The typical Google Machine Learning Engineer salary is $147,218. Machine Learning Engineer salaries at Google can range from $110,000 – $152,183.

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Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.

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This blog covers Machine Learning 101, Top 20 AWS Certified Machine Learning Specialty Questions and Answers, Top 20 Google Professional Machine Learning Engineer Sample Questions, Machine Learning Quizzes, Machine Learning Q&A, Top 10 Machine Learning Algorithms, Machine Learning Latest Hot News, Machine Learning Demos (Ex: Tensorflow Demos)

Below are the Top 100 AWS Certified Machine Learning Specialty Questions and Answers Dumps.

Top

 

Question1: A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?

A) Use Amazon SageMaker Pipe mode.
B) Use Amazon Machine Learning to train the models.
C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
D) Use AWS Glue to transform the CSV dataset to the JSON format.
ANSWER1:

A

Notes/Hint1:


Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. (Refer to this link for supporting information.) In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure.

Reference1: Amazon SageMaker

Question 2) A local university wants to track cars in a parking lot to determine which students are parking in the lot. The university is wanting to ingest videos of the cars parking in near-real time, use machine learning to identify license plates, and store that data in an AWS data store. Which solution meets these requirements with the LEAST amount of development effort?

A) Use Amazon Kinesis Data Streams to ingest the video in near-real time, use the Kinesis Data Streams consumer integrated with Amazon Rekognition Video to process the license plate information, and then store results in DynamoDB.

B) Use Amazon Kinesis Video Streams to ingest the videos in near-real time, use the Kinesis Video Streams integration with Amazon Rekognition Video to identify the license plate information, and then store the results in DynamoDB.

C) Use Amazon Kinesis Data Streams to ingest videos in near-real time, call Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

D) Use Amazon Kinesis Firehose to ingest the video in near-real time and outputs results onto S3. Set up a Lambda function that triggers when a new video is PUT onto S3 to send results to Amazon Rekognition to identify license plate information, and then store results in DynamoDB.

Answer 2)

B

Notes/Hint2)

Kinesis Video Streams is used to stream videos in near-real time. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream. After the videos have been processed by Rekognition we can output the results in DynamoDB.

Reference: Kinesis Video Streams

Question 3) A term frequency–inverse document frequency (tf–idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:

1. Please call the number below.
2. Please do not call us. What are the dimensions of the tf–idf matrix?
A) (2, 16)
B) (2, 8)
C) (2, 10)
D) (8, 10)

ANSWER3:

A

Notes/Hint3:

There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16). The phrases are “Please call the number below” and “Please do not call us.” Each word individually (unigram) is “Please,” “call,” ”the,” ”number,” “below,” “do,” “not,” and “us.” The unique bigrams are “Please call,” “call the,” ”the number,” “number below,” “Please do,” “do not,” “not call,” and “call us.” The tf–idf vectorizer is described at this link.

Reference3:  tf-idf vertorizer

Question 4: A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? 

A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
 

ANSWER4:

B

Notes/Hint4:

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
Reference4:  Glue

Question 5) Which service in the Kinesis family allows you to easily load streaming data into data stores and analytics tools?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Data Analytics
D) Kinesis Video Streams
 

ANSWER5:

A

Notes/Hint5:

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination – places like S3, Redshift, Elastisearch, and Splunk Instances.

Reference 5): Kinesis Firehose

Question 6) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? 

A) Increase the learning rate. Keep the batch size the same.
B) Reduce the batch size. Decrease the learning rate.
C) Keep the batch size the same. Decrease the learning rate.
D) Do not change the learning rate. Increase the batch size.
 
Answer  6)
B
 

Notes 6)

It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the data scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum. Refer to the paper at this link for an explanation.
Reference 6) : Here

Question 7) Your organization has a standalone Javascript (Node.js) application that streams data into AWS using Kinesis Data Streams. You notice that they are using the Kinesis API (AWS SDK) over the Kinesis Producer Library (KPL). What might be the reasoning behind this?

A) The Kinesis API (AWS SDK) provides greater functionality over the Kinesis Producer Library.
B) The Kinesis API (AWS SDK) runs faster in Javascript applications over the Kinesis Producer Library.
C) The Kinesis Producer Library must be installed as a Java application to use with Kinesis Data Streams.
D) The Kinesis Producer Library cannot be integrated with a Javascript application because of its asynchronous architecture.
Answer 7)
C
Notes/Hint7:
The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.
Reference 7) KPL
 
 

Question 8) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 

1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements?
A) TN = 91, FP = 9 FN = 22, TP = 78
 B) TN = 99, FP = 1 FN = 21, TP = 79
C) TN = 96, FP = 4 FN = 10, TP = 90
D) TN = 98, FP = 2 FN = 18, TP = 82
 
Answer 8): 
D
 

Notes/Hint 8)


The following calculations are required: TP = True Positive FP = False Positive FN = False Negative TN = True Negative FN = False Negative Recall = TP / (TP + FN) False Positive Rate (FPR) = FP / (FP + TN) Cost = 5 * FP + FN A B C D Recall 78 / (78 + 22) = 0.78 79 / (79 + 21) = 0.79 90 / (90 + 10) = 0.9 82 / (82 + 18) = 0.82 False Positive Rate 9 / (9 + 91) = 0.09 1 / (1 + 99) = 0.01 4 / (4 + 96) = 0.04 2 / (2 + 98) = 0.02 Costs 5 * 9 + 22 = 67 5 * 1 + 21 = 26 5 * 4 + 10 = 30 5 * 2 + 18 = 28 Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective. For supporting information, refer to this link.
Reference 8: Here

 
 

Question 9) A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will definitely help the model detect more than 10% of fraud cases? 

A) Using undersampling to balance the dataset
B) Decreasing the class probability threshold
C) Using regularization to reduce overfitting
D) Using oversampling to balance the dataset
 

Answer  9)

B

 

Notes 9)


Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision. This is covered in the Discussion section of the paper at this link
Reference 9: Here

 
 

Question 10) A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Which method is MOST likely to detect the GREATEST number of valid fraud cases?

A) Oversampling using bootstrapping
B) Undersampling
C) Oversampling using SMOTE
D) Class weight adjustment
 

Answer  10)

C

 
Notes 10)

With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting information.
Reference 10) : Here
 

Question 11) A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%. What should the ML engineer do to minimize bias due to missing values? 

 
A) Replace each missing value by the mean or median across non-missing values in same row.
B) Delete observations that contain missing values because these represent less than 5% of the data.
C) Replace each missing value by the mean or median across non-missing values in the same column.
D) For each feature, approximate the missing values using supervised learning based on other features.
 

Answer  11)

D

 

Notes 11)

Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research. Refer to this link for an example.
Reference 11): Here

 

Question 12) A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features? 

A) Drop the test samples with missing full review text fields, and then run through the test set.
B) Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
C) Use an algorithm that handles missing data better than decision trees.
D) Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
 
Answer  12)
B

 

 

Notes 12) 

In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field. For supporting information, refer to page 1627 at this link, and this link and this link.

Reference 12) Here

 

 

Question 13) An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task? 

 
A) Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker builtin supervised learning algorithm.
 
B) Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 
C) Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
 
D) Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
 

Answer  13)

D

 

Notes 13)

Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

Reference 13)  Amazon SageMaker
Object2Vec 

Question 14) You have been tasked with capturing two different types of streaming events. The first event type includes mission-critical data that needs to immediately be processed before operations can continue. The second event type includes data of less importance, but operations can continue without immediately processing. What is the most appropriate solution to record these different types of events?

A) Capture both events with the PutRecords API call.
B) Capture both event types using the Kinesis Producer Library (KPL).
C) Capture the mission critical events with the PutRecords API call and the second event type with the Kinesis Producer Library (KPL).
D) Capture the mission critical events with the Kinesis Producer Library (KPL) and the second event type with the Putrecords API call.
 

Answer  14)

C

 

Notes 14)

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Reference 14: KCL vs PutRecords

 

Question 15) You are collecting clickstream data from an e-commerce website to make near-real time product suggestions for users actively using the site. Which combination of tools can be used to achieve the quickest recommendations and meets all of the requirements?

A) Use Kinesis Data Streams to ingest clickstream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 
B) Use Kinesis Data Firehose to ingest click stream data, then use Kinesis Data Analytics to run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions, then use Lambda to load these results into S3.
 
C) Use Kinesis Data Streams to ingest clickstream data, then use Lambda to process that data and write it to S3. Once the data is on S3, use Athena to query based on conditions that data and make real time recommendations to users.
 
D) Use the Kinesis Data Analytics to ingest the clickstream data directly and run real time SQL queries to gain actionable insights and trigger real-time recommendations with AWS Lambda functions based on conditions.
 

Answer  15)

A

 

Notes 15)

Kinesis Data Analytics gets its input streaming data from Kinesis Data Streams or Kinesis Data Firehose. You can use Kinesis Data Analytics to run real-time SQL queries on your data. Once certain conditions are met you can trigger Lambda functions to make real time product suggestions to users. It is not important that we store or persist the clickstream data.

Reference 15: Kinesis Data Analytics

Question 16) Which service built by AWS makes it easy to set up a retry mechanism, aggregate records to improve throughput, and automatically submits CloudWatch metrics?

A) Kinesis API (AWS SDK)
B) Kinesis Producer Library (KPL)
C) Kinesis Consumer Library
D) Kinesis Client Library (KCL)

Answer  16)

B

 

Notes 16)

Although the Kinesis API built into the AWS SDK can be used for all of this, the Kinesis Producer Library (KPL) makes it easy to integrate all of this into your applications.

Reference 16:  Kinesis Producer Library (KPL) 

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Question 17) You have been tasked with capturing data from an online gaming platform to run analytics on and process through a machine learning pipeline. The data that you are ingesting is players controller inputs every 1 second (up to 10 players in a game) that is in JSON format. The data needs to be ingested through Kinesis Data Streams and the JSON data blob is 100 KB in size. What is the minimum number of shards you can use to successfully ingest this data?

A) 10 shards
B) Greater than 500 shards, so you’ll need to request more shards from AWS
C) 1 shard
D) 100 shards

Answer  17)

C

 

Notes 17)

In this scenario, there will be a maximum of 10 records per second with a max payload size of 1000 KB (10 records x 100 KB = 1000KB) written to the shard. A single shard can ingest up to 1 MB of data per second, which is enough to ingest the 1000 KB from the streaming game play. Therefor 1 shard is enough to handle the streaming data.

Reference 17: shards

Question 18) Which services in the Kinesis family allows you to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time?

A) Kinesis Streams
B) Kinesis Firehose
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer  18)

D

 

Notes 18)

Kinesis Data Analytics allows you to run real-time SQL queries on your data to gain insights and respond to events in real time.

Reference 18: Kinesis Data Analytics

 

Question 19) You are a ML specialist needing to collect data from Twitter tweets. Your goal is to collect tweets that include only the name of your company and the tweet body, and store it off into a data store in AWS. What set of tools can you use to stream, transform, and load the data into AWS with the LEAST amount of effort?

A) Setup a Kinesis Data Firehose for data ingestion and immediately write that data to S3. Next, setup a Lambda function to trigger when data lands in S3 to transform it and finally write it to DynamoDB.
B) Setup A Kinesis Data Stream for data ingestion, setup EC2 instances as data consumers to poll and transform the data from the stream. Once the data is transformed, make an API call to write the data to DynamoDB.
C) Setup Kinesis Data Streams for data ingestion. Next, setup Kinesis Data Firehouse to load that data into RedShift. Next, setup a Lambda function to query data using RedShift spectrum and store the results onto DynamoDB.
D) Create a Kinesis Data Stream to ingest the data. Next, setup a Kinesis Data Firehose and use Lambda to transform the data from the Kinesis Data Stream, then use Lambda to write the data to DynamoDB. Finally, use S3 as the data destination for Kinesis Data Firehose.
 

Answer 19)

A

Notes 19)

All of these could be used to stream, transform, and load the data into an AWS data store. The setup that requires the LEAST amount of effort and moving parts involves setting up a Kinesis Data Firehose to stream the data into S3, have it transformed by Lambda with an S3 trigger, and then written to DynamoDB.

Reference 19: Kinesis Data Firehose to stream the data into S3

Question 20) Which service in the Kinesis family allows you to build custom applications that process or analyze streaming data for specialized needs?

A) Kinesis Firehose
B) Kinesis Streams
C) Kinesis Video Streams
D) Kinesis Data Analytics

Answer 20)

B

Notes 20)

Kinesis Streams allows you to stream data into AWS and build custom applications around that streaming data.

Reference 20: Kinesis Streams

Question21: Of the following, which is an example of machine learning? (Select TWO.)

A) Calculating the shortest route from current location to the destination

B) Optimizing product pricing based on real-time sales data

C) Sentiment analysis of text on product reviews

D) A loan approval system that classifies applicants entirely based on credit score

Answer21:

B and C

Notes 21: 

Optimizing product pricing based on real-time sales data and Sentiment analysis of text on product reviews.
 

Question22:Which of the following is an appropriate use case for unsupervised learning?

A) Partitioning an image of a street scene into multiple segments

B) Finding an optimal path out of a maze

C) Identifying clusters of housing sales based on related data points

D) Analyzing sentiment of social media posts

Answer22:

C

Notes 22: 

Identifying clusters of housing sales based on related data points

Question23

Answer23:

 

Notes 23: 

Question24: A Djamgatech retail company wants to deploy a machine learning model to predict the demand for a product using sales data from the past 5 years. What is the MOST efficient solution that the company should implement first?

A) Regression

B) Multi-class classification

C) Binary class classification

D) N/A

Answer24:

A

Notes 24: 

Question25: In which phase of the ML pipeline do you analyze the business requirements and re-frame that information into a machine learning context.

A) Problem formulation

B) Model training

C) Deployment

D)

Data preprocessing

Answer25:

A

Notes 25:

AWS machine Learning Specialty Exam Prep MLS-C01

iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854

Windows: https://www.microsoft.com/en-ca/p/aws-machine-learning-mls-c01-specialty-certification-exam-prep/9n8rl80hvm4t

Android/Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

AWS MLS-C01 Machine Learning Exam Prep

Quizzes, Practice Exams: Modeling, Data Engineering, Vision, Exploratory Data Analysis, ML Ops, Cheat Sheets, ML Jobs Interview Q&A

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Earning AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

Recommend and implement the appropriate machine learning services and features for a given problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Sagemaker API Explained:

SageMaker API

AWS Certified Machine Learning Engineer Specialty Questions and Answers:

Question1: An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer1: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

Question2: Which feature of Amazon SageMaker can you use for preprocessing the data?

 

Answer2: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

Question3: What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer3: LifeCycle Configuration

Question4: How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

 

Top

Top 10 Google Professional Machine Learning Engineer Sample Questions

Question 1: You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

A. Use K-fold cross validation to understand how the model performs on different test datasets.

B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.

C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.

D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.

Answer 1)

B

Notes 1)

B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.

Question 2: You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

A. Train the model for a few iterations, and check for NaN values.
B. Train the model for a few iterations, and verify that the loss is constant.
C. Train a simple linear model, and determine if the DNN model outperforms it.
D. Train the model with no regularization, and verify that the loss function is close to zero.
 

Answer 2)

D

Notes 2)

D is correct because the test can check that the model has enough parameters to memorize the task.

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Question 3: Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?

 

A. Default Strategy; Custom tier with a single master node and four v100 GPUs.
B. One Device Strategy; Custom tier with a single master node and four v100 GPUs.
C. One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
D. Central Storage Strategy; Custom tier with a single master node and four v100 GPUs.
 

Answer 3)

D

Notes 3)

D is correct because this is the only strategy that can perform distributed training; albeit there is only a single copy of the variables on the CPU host.

Question 4: You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

 
A. Deploy model in test environment -> Validate model -> Create a new AI Platform model version
 
B. Validate model -> Deploy model in test environment -> Create a new AI Platform model version
 
C. Create a new AI Platform model version -> Validate model -> Deploy model in test environment
D. Create a new AI Platform model version – > Deploy model in test environment -> Validate model
 
Answer 4)
A
 
Notes 4)
A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production.
 
Question 5: You work for a maintenance company and have built and trained a deep learning model that identifies defects based on thermal images of underground electric cables. Your dataset contains 10,000 images, 100 of which contain visible defects. How should you evaluate the performance of the model on a test dataset?
 
A. Calculate the Area Under the Curve (AUC) value.
 
B. Calculate the number of true positive results predicted by the model.
C. Calculate the fraction of images predicted by the model to have a visible defect.
D. Calculate the Cosine Similarity to compare the model’s performance on the test dataset to the model’s performance on the training dataset.
 
Answer 5)
A
 
Notes 5)
A is correct because it is scale-invariant. AUC measures how well predictions are ranked, rather than their absolute values. AUC is also classification-threshold invariant. It measures the quality of the model’s predictions irrespective of what classification threshold is chosen.
 
Question 6: You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?

 

A. Data preparation: Daily max value feature engineering with DataPrep; Model training: AutoML classification with BQML
 
B. Data preparation: Daily min value feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
C. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
D. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
Answer 6)
D
 
Notes 6)
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
 
 
Question 7: You are an ML engineer at a media company. You need to build an ML model to analyze video content frame-by-frame, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?

 

A. Pub/Sub, Cloud Function, Cloud Vision API
 
B. Pub/Sub, Cloud IoT, Dataflow, Cloud Vision API, Cloud Logging
C. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging
D. Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging
 
Answer 7)
C
 
Notes 7)
C is correct as Video Intelligence API can find inappropriate components and other components satisfy the requirements of real-time processing and notification.
 
Question 8: You work for a large retailer. You want to use ML to forecast future sales leveraging 10 years of historical sales data. The historical data is stored in Cloud Storage in Avro format. You want to rapidly experiment with all the available data. How should you build and train your model for the sales forecast?
 
A. Load data into BigQuery and use the ARIMA model type on BigQuery ML.
B. Convert the data into CSV format and create a regression model on AutoML Tables.
C. Convert the data into TFRecords and create an RNN model on TensorFlow on AI Platform Notebooks.
D. Convert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI Platform Training.
 
Answer 8)
A
 
Notes 8)
A is correct because BigQuery ML is designed for fast and rapid experimentation and it is possible to use federated queries to read data directly from Cloud Storage. Moreover, ARIMA is considered one of the best in class for time series forecasting.
 
Question 9) You need to build an object detection model for a small startup company to identify if and where the company’s logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labelled. You need to label these pictures, and then train and deploy the model. What should you do?

 

A. Use Google Cloud’s Data Labelling Service to label your data. Use AutoML Object Detection to train and deploy the model.
B. Use Vision API to detect and identify logos in pictures and use it as a label. Use AI Platform to build and train a convolutional neural network.
 
C. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a convolutional neural network.
D. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a real time object detection model.
 
Answer 9)
A
 
Notes 9)
A is correct as this will allow you to easily create a request for a labelling task and deploy a high-performance model.
 

Question 10) You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company’s mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer’s stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

A. Automate a blend of the shortest and longest intents to be representative of all intents.
B. Automate the more complicated requests first because those require more of the agents’ time.
C. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
 
D. Automate intents in places where common words such as “payment” only appear once to avoid confusing the software.
Answer 10)
C
 
Notes 10)

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Machine Learning Q&A Part I:

Google.

Azure and AWS are second class citizens in this area.

Sure, AWS has 70% of the market.

Sure, Azure is the easiest turn key and super user friendly.

But, the king of machine learning in the cloud is GCP.

GCP = Google Cloud Platform

Google has the largest data science team in the world, not mention they have Hinton.

Let’s forgot for a minute they created TensorFlow and give it away.

Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.

The vast majority of applied machine learning is supervised and that means we need data.

Not just normal data, we need very clean highly structured data.

Where’s the easiest place in the world to upload and model a Petabyte of structured dataBigQuery of course.

Why BigQuery? I don’t have to do anything but upload my data. No spinning up RedShit clusters or whatever I have to do in Azure, just upload and massage data with my familiar SQL. If I do have to wrangle my data it won’t take my six months to update 5 rows here, minutes usually.

Then, you’ll need a front end. Cloud datalab is a Jupyter notebook, which is good because I don’t want nor do I need anything else.

Then, with a single line of code I connect by datalab (Jupyter) notebook to my data in BigQuery and build away.

I’ve worked in all three and the only thing I care about is getting to my job the fastest and right now that means I build my models in GCP.

If you’re new to machine learning don’t start in GCP or any cloud vendor for that matter. Start learning Python from the comfort of your laptop.

The course below is free to the first 20.

The Complete Python Course for Machine Learning Engineers

Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.

This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

 
 
 

The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.

Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.

The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz

The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).

The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt

I hope it will be helpful for Statistic and Machine Leaning aspirants!

Thank you!

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

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Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

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[appbox microsoftstore  9n8rl80hvm4t-mobile screenshots]

Machine Learning Q&A -Part II:

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

 

AWS machine Learning Specialty Exam Prep MLS-C01

iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854

Windows: https://www.microsoft.com/en-ca/p/aws-machine-learning-mls-c01-specialty-certification-exam-prep/9n8rl80hvm4t

Android/Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

AWS MLS-C01 Machine Learning Exam Prep

Quizzes, Practice Exams: Modeling, Data Engineering, Vision, Exploratory Data Analysis, ML Ops, Cheat Sheets, ML Jobs Interview Q&A

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Earning AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

Recommend and implement the appropriate machine learning services and features for a given problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Sagemaker API Explained:

SageMaker API

AWS Certified Machine Learning Engineer Specialty Questions and Answers:

Question1: An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer1: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

Question2: Which feature of Amazon SageMaker can you use for preprocessing the data?

 

Answer2: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

Question3: What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer3: LifeCycle Configuration

Question4: How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

 

Top

Top 10 Google Professional Machine Learning Engineer Sample Questions

Question 1: You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

A. Use K-fold cross validation to understand how the model performs on different test datasets.

B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.

C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.

D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.

Answer 1)

B

Notes 1)

B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.

Question 2: You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

A. Train the model for a few iterations, and check for NaN values.
B. Train the model for a few iterations, and verify that the loss is constant.
C. Train a simple linear model, and determine if the DNN model outperforms it.
D. Train the model with no regularization, and verify that the loss function is close to zero.
 

Answer 2)

D

Notes 2)

D is correct because the test can check that the model has enough parameters to memorize the task.

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Question 3: Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training. What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?

 

A. Default Strategy; Custom tier with a single master node and four v100 GPUs.
B. One Device Strategy; Custom tier with a single master node and four v100 GPUs.
C. One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
D. Central Storage Strategy; Custom tier with a single master node and four v100 GPUs.
 

Answer 3)

D

Notes 3)

D is correct because this is the only strategy that can perform distributed training; albeit there is only a single copy of the variables on the CPU host.

Question 4: You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

 
A. Deploy model in test environment -> Validate model -> Create a new AI Platform model version
 
B. Validate model -> Deploy model in test environment -> Create a new AI Platform model version
 
C. Create a new AI Platform model version -> Validate model -> Deploy model in test environment
D. Create a new AI Platform model version – > Deploy model in test environment -> Validate model
 
Answer 4)
A
 
Notes 4)
A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production.
 
Question 5: You work for a maintenance company and have built and trained a deep learning model that identifies defects based on thermal images of underground electric cables. Your dataset contains 10,000 images, 100 of which contain visible defects. How should you evaluate the performance of the model on a test dataset?
 
A. Calculate the Area Under the Curve (AUC) value.
 
B. Calculate the number of true positive results predicted by the model.
C. Calculate the fraction of images predicted by the model to have a visible defect.
D. Calculate the Cosine Similarity to compare the model’s performance on the test dataset to the model’s performance on the training dataset.
 
Answer 5)
A
 
Notes 5)
A is correct because it is scale-invariant. AUC measures how well predictions are ranked, rather than their absolute values. AUC is also classification-threshold invariant. It measures the quality of the model’s predictions irrespective of what classification threshold is chosen.
 
Question 6: You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?

 

A. Data preparation: Daily max value feature engineering with DataPrep; Model training: AutoML classification with BQML
 
B. Data preparation: Daily min value feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
C. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
D. Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
Answer 6)
D
 
Notes 6)
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
 
 
Question 7: You are an ML engineer at a media company. You need to build an ML model to analyze video content frame-by-frame, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?

 

A. Pub/Sub, Cloud Function, Cloud Vision API
 
B. Pub/Sub, Cloud IoT, Dataflow, Cloud Vision API, Cloud Logging
C. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging
D. Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging
 
Answer 7)
C
 
Notes 7)
C is correct as Video Intelligence API can find inappropriate components and other components satisfy the requirements of real-time processing and notification.
 
Question 8: You work for a large retailer. You want to use ML to forecast future sales leveraging 10 years of historical sales data. The historical data is stored in Cloud Storage in Avro format. You want to rapidly experiment with all the available data. How should you build and train your model for the sales forecast?
 
A. Load data into BigQuery and use the ARIMA model type on BigQuery ML.
B. Convert the data into CSV format and create a regression model on AutoML Tables.
C. Convert the data into TFRecords and create an RNN model on TensorFlow on AI Platform Notebooks.
D. Convert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI Platform Training.
 
Answer 8)
A
 
Notes 8)
A is correct because BigQuery ML is designed for fast and rapid experimentation and it is possible to use federated queries to read data directly from Cloud Storage. Moreover, ARIMA is considered one of the best in class for time series forecasting.
 
Question 9) You need to build an object detection model for a small startup company to identify if and where the company’s logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labelled. You need to label these pictures, and then train and deploy the model. What should you do?

 

A. Use Google Cloud’s Data Labelling Service to label your data. Use AutoML Object Detection to train and deploy the model.
B. Use Vision API to detect and identify logos in pictures and use it as a label. Use AI Platform to build and train a convolutional neural network.
 
C. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a convolutional neural network.
D. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use AI Platform to build and train a real time object detection model.
 
Answer 9)
A
 
Notes 9)
A is correct as this will allow you to easily create a request for a labelling task and deploy a high-performance model.
 

Question 10) You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company’s mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer’s stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

A. Automate a blend of the shortest and longest intents to be representative of all intents.
B. Automate the more complicated requests first because those require more of the agents’ time.
C. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
 
D. Automate intents in places where common words such as “payment” only appear once to avoid confusing the software.
Answer 10)
C
 
Notes 10)

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Machine Learning Q&A Part I:

Google.

Azure and AWS are second class citizens in this area.

Sure, AWS has 70% of the market.

Sure, Azure is the easiest turn key and super user friendly.

But, the king of machine learning in the cloud is GCP.

GCP = Google Cloud Platform

Google has the largest data science team in the world, not mention they have Hinton.

Let’s forgot for a minute they created TensorFlow and give it away.

Let’s just talk about building a real world model with data that doesn’t fit into a excel spreadsheet.

The vast majority of applied machine learning is supervised and that means we need data.

Not just normal data, we need very clean highly structured data.

Where’s the easiest place in the world to upload and model a Petabyte of structured dataBigQuery of course.

Why BigQuery? I don’t have to do anything but upload my data. No spinning up RedShit clusters or whatever I have to do in Azure, just upload and massage data with my familiar SQL. If I do have to wrangle my data it won’t take my six months to update 5 rows here, minutes usually.

Then, you’ll need a front end. Cloud datalab is a Jupyter notebook, which is good because I don’t want nor do I need anything else.

Then, with a single line of code I connect by datalab (Jupyter) notebook to my data in BigQuery and build away.

I’ve worked in all three and the only thing I care about is getting to my job the fastest and right now that means I build my models in GCP.

If you’re new to machine learning don’t start in GCP or any cloud vendor for that matter. Start learning Python from the comfort of your laptop.

The course below is free to the first 20.

The Complete Python Course for Machine Learning Engineers

Here, I want to share the best research paper on Machine Learning classification methods, titled ‘Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?’, published in the ‘Journal of Machine Learning Research’.

This paper nicely explained 179 classification techniques and applied them on 121 data sets thus sharing small summary of the paper:

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

 
 
 

The paper evaluated 179 classifiers arising from 17 ML families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest neighbours, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R ( with and without the caret package), C and Matlab, including all the relevant classifiers available today.

Experiments used total 121 data sets , which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behaviour, not dependent on the data set collection.

The whole data set and partitions are available from: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz

The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package).

The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

You can see the table with the complete results: http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/results.txt

I hope it will be helpful for Statistic and Machine Leaning aspirants!

Thank you!

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

[appbox appstore 1560083470-iphone screenshots]
[appbox googleplay com.awssolutionarchitectassociateexampreppro.app]

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

Top

[appbox appstore 1611045854-iphone screenshots]

[appbox microsoftstore  9n8rl80hvm4t-mobile screenshots]

Machine Learning Q&A -Part II:

 
 
 

At a high level, these skills are a combination of software and data engineering.

The persons that are more appropriate to do this job are a data engineer and/or a machine learning engineer.

That being said, if you work at a startup or happen to be in a small company and need to put the models into production yourself, here are the top skills you need to get:

  • Well structured code: it doesn’t need to be perfect but at least can be understood and updated by other team members. Avoid spaghetti code[1] as the plague.
  • Add logs: if you are a Python user, the logging[2] module is your friend. Avoid print statements at any cost.
  • Model versioning: add a hash key to your different models. You will thank me later.
  • Metadata everywhere: save as much data about your models and ML experiments as you can (running time, hyperparameters, used features, CV scores, and so on). You will thank me later, again.
  • Monitor performances: execution time and statistical scores of your models.
  • Data and models management: store the necessary data and models somewhere that is available to everyone (S3[3] for example). Avoid uploading these to your VCS[4] system. Don’t share them using Slack or Drive. I won’t judge you though, I do it sometimes (read often). Read more here …..

Some of the mistakes that might involve during building a machine learning model (I can think of) are listed here:

  1. Not understanding the structure of the dataset
  2. Not giving proper care during features selection
  3. Leaving out categorical features and considering just numerical variables
  4. Falling into dummy variable trap
  5. Selection of inefficient machine learning algorithm
  6. Not trying out various ML algorithms for building the model based on structure of data.
  7. Improper tuning of model parameters
  8. Most importantly: Building an idiotstic imperfect model i.e. suppose we have a classification problem with 99% chances of falling into class1 and remaining to class2. The built model may develop a mapping function which all the time for all data inputs, may predict the result to be class1. Well, one might say his/her model has 99% accuracy. But in reality the 1% class2 case hasn’t been included in the model. So this must be taken into consideration.
  9. Read more here…

Basically, data mining is a key aspect of data analytics. Some even consider the former as essential to execute before the latter. While data analytics is the complete package and involves most components needed to examine a data set and extract valuable information, data mining focuses specifically on identifying hidden patterns.

That’s just the surface-level comparison though. The image above gives an overview of how the two differ.

One such difference is the presence of a hypothesis. Data analytics usually requires coming up with one, as it aims to find specific answers. Data mining, on the other hand, generally doesn’t need one to test or prove. The expected output are patterns or trends, which doesn’t require coming up with a statement or fact to test.

However, that doesn’t mean you mine data blindly. You still have a goal, whether it’s to come up with a recommender system or identify predictors of a certain dimension. Ultimately though, you strive to come up with data patterns or trends. For data analysis on the other hand, you’re expected to come up with valuable and actionable insights, usually in relation to a predetermined hypothesis. Read more here ….

The data science life cycle is not something well-defined like the software development life-cycle, and there is no ‘one-size-fits-all’ solution for data science projects. Every step in the life-cycle of a data science project depends on various data scientist skills and data science tools. The typical life-cycle of a data science project involves jumping back and forth among various interdependent science tasks using a variety of tools, techniques, programming, etc.

Thus, the data science life-cycle can include the following steps:

  1. Business requirement understanding.
  2. Data collection.
  3. Data cleaning.
  4. Data analysis.
  5. Modeling.
  6. Performance evaluation.
  7. Communicating with stakeholders.
  8. Deployment.
  9. Real-world testing.
  10. Business buy-in.
  11. Support and maintenance.

Looks neat, but here is the scheme to visualize how it is happening in reality:

Agile development processes, especially continuous delivery lends itself well to the data science project life-cycle. The early comparison helps the data science team to change approaches, refine hypotheses and even discard the project if the business case is nonviable or the benefits from the predictive models are not worth the effort to build it.

Read more here….

 

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AWS machine Learning Specialty Exam Prep MLS-C01

iOs: https://apps.apple.com/ca/app/aws-machine-learning-prep-pro/id1611045854

Windows: https://www.microsoft.com/en-ca/p/aws-machine-learning-mls-c01-specialty-certification-exam-prep/9n8rl80hvm4t

Android/Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

AWS MLS-C01 Machine Learning Exam Prep

Quizzes, Practice Exams: Modeling, Data Engineering, Vision, Exploratory Data Analysis, ML Ops, Cheat Sheets, ML Jobs Interview Q&A

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Earning AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

Recommend and implement the appropriate machine learning services and features for a given problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Sagemaker API Explained:

SageMaker API

AWS Certified Machine Learning Engineer Specialty Questions and Answers:

Question1: An advertising and analytics company uses machine learning to predict user response to online advertisements using a custom XGBoost model. The company wants to improve its ML pipeline by porting its training and inference code, written in R, to Amazon SageMaker, and do so with minimal changes to the existing code.

Answer1: Use the Build Your Own Container (BYOC) Amazon Sagemaker option.
Create a new docker container with the existing code. Register the container in Amazon Elastic Container registry. with the existing code. Register the container in Amazon Elastic Container Registry. Finally run the training and inference jobs using this container.

Question2: Which feature of Amazon SageMaker can you use for preprocessing the data?

 

Answer2: Amazon Sagemaker Notebook instances

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.  You can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.

Question3: What setting, when creating an Amazon SageMaker notebook instance, can you use to install libraries and import data?

Answer3: LifeCycle Configuration

Question4: How to Choose the right Sagemaker built-in algorithm?

How to chose the right built in algorithm in SageMaker?
How to chose the right built in algorithm in SageMaker?
Guide to choosing the right unsupervised learning algorithm
Guide to choosing the right unsupervised learning algorithm

 

Choosing the right  ML algorithm based on Data Type
Choosing the right ML algorithm based on Data Type

 

Choosing the right ML algo based on data type
Choosing the right ML algo based on data type

This is a general guide for choosing which algorithm to use depending on what business problem you have and what data you have. 

 

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Top 10 Google Professional Machine Learning Engineer Sample Questions

Question 1: You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

A. Use K-fold cross validation to understand how the model performs on different test datasets.

B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.

C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.

D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.

Answer 1)

B

Notes 1)

B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.

Question 2: You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?