What is the Best Machine Learning Algorithms for Imbalanced Datasets

Machine Learning Algorithms and Imbalanced Datasets

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

What is the Best Machine Learning Algorithms for Imbalanced Datasets?

In machine learning, imbalanced datasets are those where one class heavily outnumbers the others. This can be due to the nature of the problem or simply because more data is available for one class than the others. Either way, imbalanced datasets can pose a challenge for machine learning algorithms. In this blog post, we’ll take a look at which machine learning algorithms are best suited for imbalanced datasets and why they tend to perform better than others.

 For example, in a binary classification problem, if there are 100 observations, and only 10 of them are positive (the rest are negatives), then we say that the dataset is imbalanced. The ratio of positive to negative cases is 1:10. 

What is the Best Machine Learning Algorithms for Imbalanced Datasets
What is the Best Machine Learning Algorithms for Imbalanced Datasets

There are a few reasons why some machine learning algorithms tend to perform better on imbalanced datasets than others. First, certain algorithms are designed to handle imbalanced datasets. Second, some algorithms are more robust to outliers, which can be more common in imbalanced datasets. And third, some algorithms are better able to learn from a limited amount of data, which can be an issue when one class is heavily outnumbered by the others.

Some of the best machine learning algorithms for imbalanced datasets include:

Support Vector Machines (SVMs),
Decision Trees,
Random Forests,
– Naive Bayes Classifiers,
k-Nearest Neighbors (kNN),

Of these, SVMs tend to be the most popular choice as they are specifically designed to handle imbalanced datasets. SVMs work by finding a hyperplane that maximizes the margin between the two classes. This helps to reduce overfitting and improve generalization. Decision trees and random forests are also popular choices as they are less sensitive to outliers than other algorithms such as linear regression. Naive Bayes classifiers are another good choice as they are able to learn from a limited amount of data. kNN is also a good choice as it is not sensitive to outliers and is able to learn from a limited amount of data. However, it can be computationally intensive for large datasets.

There are two main types of machine learning algorithms: supervised and unsupervised. Supervised algorithms tend to perform better on imbalanced datasets than unsupervised algorithms. In this blog post, we will discuss why this is so and look at some examples.

Supervised Algorithms
Supervised algorithms are those where the target variable is known. In other words, we have training data where the correct answers are already given. The algorithm then learns from this data and is able to generalize to new data. Some examples of supervised algorithms are regression and classification.

Unsupervised Algorithms
Unsupervised algorithms are those where the target variable is not known. With unsupervised algorithms, we only have input data, without any corresponding output labels. The algorithm has to learn from the data itself without any guidance. Some examples of unsupervised algorithms are clustering and dimensionality reduction.

Why Supervised Algorithms Perform Better on Imbalanced Datasets
The reason why supervised algorithms perform better on imbalanced datasets is because they can learn from the training data which cases are more important. With unsupervised algorithms, all data points are treated equally, regardless of whether they are in the minority or majority class.

For example, in a binary classification problem with an imbalanced dataset, let’s say that we want to predict whether a customer will default on their loan payment or not. We have a training dataset of 1000 customers, out of which only 100 (10%) have defaulted on their loan in the past.

If we use a supervised algorithm like logistic regression, the algorithm will learn from the training data that defaulting on a loan is rare (since only 10% of cases in the training data are Positive). This means that it will be more likely to predict correctly that a new customer will not default on their loan (since this is the majority class in the training data).
However, if we use an unsupervised algorithm like k-means clustering, all data points will be treated equally since there is no target variable to guide the algorithm. This means that it might incorrectly cluster together customers who have defaulted on their loans with those who haven’t since there is no guidance provided by a target variable.

Conclusion:
In conclusion, supervised machine learning algorithms tend to perform better on imbalanced datasets than unsupervised machine learning algorithms because they can learn from the training data which cases are more important. 

Some machine learning algorithms tend to perform better on highly imbalanced datasets because they are designed to deal with imbalance or because they can learn from both classes simultaneously. If you are working with a highly imbalanced dataset, then you should consider using one of these algorithms.

Thanks for reading!

How are machine learning techniques being used to address unstructured data challenges?

Machine learning techniques are being used to address unstructured data challenges in a number of ways:

  1. Natural language processing (NLP): NLP algorithms can be used to extract meaningful information from unstructured text data, such as emails, documents, and social media posts. NLP algorithms can be trained to classify text data, identify key terms and concepts, and extract structured data from unstructured text.
  2. Image recognition: Machine learning algorithms can be used to analyze and classify images, enabling the automatic identification and classification of objects, people, and other elements in images. This can be useful for tasks such as image tagging and search, as well as for applications such as security and surveillance.
  3. Audio and speech recognition: Machine learning algorithms can be used to analyze and classify audio data, enabling the automatic transcription and translation of spoken language. This can be useful for tasks such as speech-to-text transcription, as well as for applications such as call center automation and language translation.
  4. Video analysis: Machine learning algorithms can be used to analyze and classify video data, enabling the automatic detection and classification of objects, people, and other elements in video. This can be useful for tasks such as video tagging and search, as well as for applications such as security and surveillance.

Overall, machine learning techniques are being used in a wide range of applications to extract meaningful information from unstructured data, and to enable the automatic classification and analysis of data in a variety of formats.

How is AI and machine learning impacting application development today?

Artificial intelligence (AI) and machine learning are having a significant impact on application development today in a number of ways:

  1. Enabling new capabilities: AI and machine learning algorithms can be used to enable applications to perform tasks that would be difficult or impossible for humans to do. For example, AI-powered applications can be used to analyze and classify large amounts of data, or to automate complex decision-making processes.
  2. Improving performance: AI and machine learning algorithms can be used to optimize the performance of applications, making them faster, more efficient, and more accurate. For example, machine learning algorithms can be used to improve the accuracy of predictive models, or to optimize the performance of search algorithms.
  3. Streamlining development: AI and machine learning algorithms can be used to automate various aspects of application development, such as testing, debugging, and deployment. This can help to streamline the development process and reduce the time and resources needed to build and maintain applications.
  4. Enhancing user experiences: AI and machine learning algorithms can be used to enhance the user experience of applications, by providing personalized recommendations, recommendations, or by enabling applications to anticipate and respond to the needs and preferences of users.

Overall, AI and machine learning are having a significant impact on application development today, and they are likely to continue to shape the way applications are built and used in the future.

How will advancements in artificial intelligence and machine learning shape the future of work and society?

Advancements in artificial intelligence (AI) and machine learning are likely to shape the future of work and society in a number of ways. Some potential impacts include:

Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6
Get 20% off Google Workspace (Google Meet)  Business Plan (AMERICAS) with  the following codes:  C37HCAQRVR7JTFK Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)

Active Anti-Aging Eye Gel, Reduces Dark Circles, Puffy Eyes, Crow's Feet and Fine Lines & Wrinkles, Packed with Hyaluronic Acid & Age Defying Botanicals

  1. Automation: AI and machine learning algorithms can be used to automate tasks that are currently performed by humans, such as data entry, customer service, and manufacturing. This could lead to changes in the types of jobs that are available and the skills that are in demand, as well as to increased productivity and efficiency.
  2. Job displacement: While automation may create new job opportunities, it could also lead to job displacement, particularly for workers in industries that are more susceptible to automation. This could lead to social and economic challenges, including unemployment and income inequality.
  3. Increased efficiency: AI and machine learning algorithms can be used to optimize and streamline business processes, leading to increased efficiency and productivity. This could lead to economic growth and innovation, and could also help to reduce costs for businesses and consumers.
  4. Enhanced decision-making: AI and machine learning algorithms can be used to analyze large amounts of data and make more informed and accurate decisions. This could lead to improved outcomes in fields such as healthcare, finance, and education, and could also help to reduce bias and improve fairness.

Overall, the impact of AI and machine learning on the future of work and society is likely to be significant and complex, with both potential benefits and challenges. It will be important to consider and address these impacts as these technologies continue to advance and become more widely adopted.

  • [P] seemore: Implement a Vision Language Model from Scratch
    by /u/avi1x (Machine Learning) on April 22, 2024 at 3:10 pm

    Hi all, I implemented a vision language model consisting of an image encoder, a multimodal projection module and a decoder language model in pure pytorch. Think of this as a simplified version of what you see in GPT-4 or Claude 3 in terms of vision capabilities demonstrated by a language model (think moondream 2 or LLaVA when it comes to Open Source Models). The name ‘seemore’ is my way of paying homage to Andrej Karpathy’s project ‘makemore’ because here I use a character level autoregressive language model much like in his nanoGPT/ makemore implementation. My goal is for this to be a hackable implementation that people use to understand how this really works and improve upon. I foresee more and more of these models coming out throughout the year. The blog with a link to the repo is here: https://huggingface.co/blog/AviSoori1x/seemore-vision-language-model Hope this is helpful or at least interesting! (posted this on LocalLlama but figured this is just as applicable to ML in general) submitted by /u/avi1x [link] [comments]

  • [D] Llama-3 may have just killed proprietary AI models
    by /u/madredditscientist (Machine Learning) on April 22, 2024 at 3:08 pm

    Meta released Llama-3 only three days ago, and it already feels like the inflection point when open source models finally closed the gap with proprietary models. The initial benchmarks show that Llama-3 70B comes pretty close to GPT-4 in many tasks: The official Meta page only shows that Llama-3 outperforms Gemini 1.5 and Claude Sonnet. Artificial Analysis shows that Llama-3 is in-between Gemini-1.5 and Opus/GPT-4 for quality. On LMSYS Chatbot Arena Leaderboard, Llama-3 is ranked #5 while current GPT-4 models and Claude Opus are still tied at #1. The even more powerful Llama-3 400B+ model is still in training and is likely to surpass GPT-4 and Opus once released. Meta vs OpenAI Some speculate that Meta's goal from the start was to target OpenAI with a "scorched earth" approach by releasing powerful open models to disrupt the competitive landscape and avoid being left behind in the AI race. Meta can likely outspend OpenAI on compute and talent: OpenAI makes an estimated revenue of $2B and is likely unprofitable. Meta generated a revenue of $134B and profits of $39B in 2023. Meta's compute resources likely outrank OpenAI by now. Open source likely attracts better talent and researchers. One possible outcome could be the acquisition of OpenAI by Microsoft to catch up with Meta. Google is also making moves into the open model space and has similar capabilities to Meta. It will be interesting to see where they fit in. The Winners: Developers and AI Product Startups I recently wrote about the excitement of building an AI startup right now, as your product automatically improves with each major model advancement. With the release of Llama-3, the opportunities for developers are even greater: No more vendor lock-in. Instead of just wrapping proprietary API endpoints, developers can now integrate AI deeply into their products in a very cost-effective and performant way. There are already over 800 llama-3 models variations on Hugging Face, and it looks like everyone will be able to fine-tune for their us-cases, languages, or industry. Faster, cheaper hardware: Groq can now generate 800 llama-3 tokens per second at a small fraction of the GPT costs. Near-instant LLM responses at low prices are on the horizon. Open source multimodal models for vision and video still have to catch up, but I expect this to happen very soon. The release of Llama-3 marks a significant milestone in the democratization of AI, but it's probably too early to declare the death of proprietary models. Who knows, maybe GPT-5 will surprise us all and surpass our imaginations of what transformer models can do. These are definitely super exciting times to build in the AI space! Original Blog Post submitted by /u/madredditscientist [link] [comments]

  • [D] Seeking Thesis Topic Suggestions for Executive Master in AI
    by /u/Away-Jaguar-816 (Machine Learning) on April 22, 2024 at 2:52 pm

    Hello fellow engineers, I'm currently pursuing an Executive Master in AI and Big Data. I'm an Embedded Systems Engineer from Morocco, working in the automotive sector for an American company as a Software Engineer. My aim with this Master's is to transition into a machine learning engineering role. I'm looking for thesis topic suggestions that are market-oriented, focusing on the skills essential for a machine learning engineer position. While it doesn't have to be automotive-specific, I'd like it to be realistic, a subject capable to be completed in more or less 3 months. Your insights and recommendations would be highly valued! Thank you in advance for your help. submitted by /u/Away-Jaguar-816 [link] [comments]

  • [D] Overview of Data Science in the post-transformer area?
    by /u/CodingButStillAlive (Machine Learning) on April 22, 2024 at 1:13 pm

    I think I stopped using scikit-learn and following along topics like Statistical Learning since the rise of transformer models and other deep learning methods. Recently, I dealt with diffusion models for video synthesis. However, as I am still a Data Scientist, I wonder where to find a good book / online course about Data Science in the post-deep learning era, as I would call it. Something like the books by Aurelién Geron, I suppose. Or, is datacamp still worth it? Any suggestions? 🙂 submitted by /u/CodingButStillAlive [link] [comments]

  • [D] Please recommend recent ML talk or interview on YouTube
    by /u/20231027 (Machine Learning) on April 22, 2024 at 11:00 am

    These recent talks were very illuminating Andre conversation at the Sequoia -https://youtu.be/c3b-JASoPi0?si=0R9LFUokb2_GPLlY Kaiming tracing history of Computer vision networks - https://youtu.be/D_jt-xO_RmI?si=uaAtrCWeIXwHBiQ2 Do you have other recommendations? submitted by /u/20231027 [link] [comments]

  • [P] negative sampling from small negative observations for recommendation system
    by /u/No_Carpenter_9469 (Machine Learning) on April 22, 2024 at 10:28 am

    I am working on a recommendation system on a user item interaction matrix based on implicit feedback (binary), and I have positive observed interactions and a very small amount of negative observed interactions. For both user and item there are vector features available as embeddings. Are there any methods that I can perform negative sampling through the negative observations? I have heard of methods like contrastive learning but not aware of ways to integrate existing negative observations. submitted by /u/No_Carpenter_9469 [link] [comments]

  • [R] Recurrent Memory has broken the limits of Context Length for Transformer Neural Networks
    by /u/AIRI_Institute (Machine Learning) on April 22, 2024 at 10:08 am

    The researchers segmented the sequence and added special memory tokens to the input: memory states from the output of the previous segment became inputs for the next one. Thus, a whole transformer acts as a recurrent cell, and memory serves as the recurrent state of the network. This approach was called Recurrent Memory Transformer (RMT). The authors augmented small transformer models like BERT and GPT-2 with this memory and tested them on various question-answering tasks where facts needed for answering are somewhere in the text. It was found that using recurrent memory significantly increases the length of the input sequence while maintaining satisfactory neural network performance accuracy. In their experiments, scientists were able to extend this value to 2 million tokens. According to the authors, there are no fundamental limitations for this value to increase further, as the computational complexity of RMT grows linearly with the number of tokens. The accuracy of the pre-trained BERT model augmented with RMT on three tasks vs the number of tokens in the input sequence. The gray numbers indicate the GPU memory consumption, and the vertical lines represent the length limits in SOTA models (as of the end of 2023) The research was published in the proceedings of the AAAI-24 conference, additional details are provided in the preprint, and the code is available on GitHub. submitted by /u/AIRI_Institute [link] [comments]

  • [D] Copy Mechanism in transformers, help!!
    by /u/SnooOnions9136 (Machine Learning) on April 22, 2024 at 8:59 am

    Hi everyone, I was reading this paper on in-context learning: https://arxiv.org/abs/2212.07677 . In section 4 it refers to this “copy mechanism” but I’m struggling to understand what it actually does… My question is unrelated to the specifics of the paper, I’d like to know what is in general the copy mechanism !!! Can someone help please? :))))) submitted by /u/SnooOnions9136 [link] [comments]

  • [R] TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
    by /u/SeawaterFlows (Machine Learning) on April 22, 2024 at 8:46 am

    Paper: https://arxiv.org/abs/2404.11912 Code: https://github.com/Infini-AI-Lab/TriForce Project page: https://infini-ai-lab.github.io/TriForce/ Abstract: With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length. Due to the auto-regressive nature of LLMs, the entire KV cache will be loaded for every generated token, resulting in low utilization of computational cores and high latency. While various compression methods for KV cache have been proposed to alleviate this issue, they suffer from degradation in generation quality. We introduce TriForce, a hierarchical speculative decoding system that is scalable to long sequence generation. This approach leverages the original model weights and dynamic sparse KV cache via retrieval as a draft model, which serves as an intermediate layer in the hierarchy and is further speculated by a smaller model to reduce its drafting latency. TriForce not only facilitates impressive speedups for Llama2-7B-128K, achieving up to 2.31× on an A100 GPU but also showcases scalability in handling even longer contexts. For the offloading setting on two RTX 4090 GPUs, TriForce achieves 0.108s/token—only half as slow as the auto-regressive baseline on an A100, which attains 7.78× on our optimized offloading system. Additionally, TriForce performs 4.86× than DeepSpeed-Zero-Inference on a single RTX 4090 GPU. TriForce's robustness is highlighted by its consistently outstanding performance across various temperatures. The code is available at this https URL. submitted by /u/SeawaterFlows [link] [comments]

  • [R] Many-Shot In-Context Learning
    by /u/SeawaterFlows (Machine Learning) on April 22, 2024 at 8:31 am

    Paper: https://arxiv.org/abs/2404.11018 Abstract: Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples -- the many-shot regime. Going from few-shot to many-shot, we observe significant performance gains across a wide variety of generative and discriminative tasks. While promising, many-shot ICL can be bottlenecked by the available amount of human-generated examples. To mitigate this limitation, we explore two new settings: Reinforced and Unsupervised ICL. Reinforced ICL uses model-generated chain-of-thought rationales in place of human examples. Unsupervised ICL removes rationales from the prompt altogether, and prompts the model only with domain-specific questions. We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks. Finally, we demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases and can learn high-dimensional functions with numerical inputs. Our analysis also reveals the limitations of next-token prediction loss as an indicator of downstream ICL performance. submitted by /u/SeawaterFlows [link] [comments]

  • [P] Zero shot logo detection
    by /u/CommercialFragrant (Machine Learning) on April 22, 2024 at 5:49 am

    I'm trying to create a web app that recognizes logos of brands in images. I've tried using Microsoft Azure Computer Vision/Custom Vision API but the results are not satisfactory. I have read about Yolo and Yolo world. If you've ever used them in your projects, can you help me see if zero shot logo detection can be achieved by this? submitted by /u/CommercialFragrant [link] [comments]

  • [Discussion]What is the reality for someone with extensive SWE experience who is trying to crack into ML engineering or Data engineering by doing personal projects and creating a portfolio. Is that even a realistic goal?
    by /u/Emergency-Director53 (Machine Learning) on April 22, 2024 at 5:11 am

    Looking for brutally honest opinions. Is the reality different for data engineers as I find the supply demand makes DE attractive currently ? submitted by /u/Emergency-Director53 [link] [comments]

  • [D] Looking for research on Transformers applied to niche tasks, not language. (ex. AlphaGeometry)
    by /u/RedditLovingSun (Machine Learning) on April 22, 2024 at 4:31 am

    I know there's been some research from google on using the transformer architecture for things like Geometry and Chess. Thinking of transformers as general algorithm learners interests me in learning about what other things they can be applied to and examining how they perform. Could the architecture learn to solve, for example, mazes? If it did would it's methods resemble A* or instead some other unknown algorithm? Can it converge on 'simulating' the most efficient algorithm for a given task or will it get stuck on inefficient methods (and if it did is that an architectural limitation)?. What roles do datasets have on achieving OOD generalization for tasks like this? Looking for niche and creative applications of transformers to do some more digging into these questions. Lemme know if you know of any good papers! (side note: an side interesting project may be to build a vector db of arvix paper abstracts so one could search for questions like this semantically). submitted by /u/RedditLovingSun [link] [comments]

  • [D] [R] AI logo generator Looka’s ML model
    by /u/Vishesh9096 (Machine Learning) on April 22, 2024 at 4:28 am

    I came across this AI logo generator website Looka. Does anyone have an idea of how does it actually work? What ML models are used to generate logos so fast or are there premade templates ? I also trued stable diffusion for generating logos, but it takes time and also dosent generates logo that good. submitted by /u/Vishesh9096 [link] [comments]

  • [D] Direct Preference Policy (DPO) - SFT dataset
    by /u/nohodlnodough (Machine Learning) on April 22, 2024 at 4:17 am

    In the dpo paper, the authors recommended to do SFT prior to doing DPO to prevent distribution shift and also demonstrated the discrepancy in performance for non-SFT and SFT in the new paper: https://arxiv.org/pdf/2404.12358.pdf However, i am slightly unsure about whats the rule for curating the preference dataset using a SFT-ed model. Does it mean that before doing DPO, the ref model HAS to be SFTed on the same prompts (x) of the preference dataset/similar distribution dataset? OR the preference dataset has to be curated from the ref model? The latter would mean that you could do SFT on any dataset so long as the pref dataset is curated using the SFT-ed model and not using any available pref dataset you find online, which most likely is curated using some unknown policy. While the former is saying that the ref policy has to be SFTed on the same distribution of the pref dataset (ie similar prompt types), meaning this is just an additional SFT step on the pref dataset's chosen response as compared to the previous case. What are your thoughts on this? submitted by /u/nohodlnodough [link] [comments]

  • [D] Is the AI Workforce or Companies More Distributed Than Those in Other Tech Sectors?
    by /u/digital-bolkonsky (Machine Learning) on April 22, 2024 at 2:36 am

    submitted by /u/digital-bolkonsky [link] [comments]

  • [D] Recommendation for a language modeling dataset that breaks down into a large number of sub-domains
    by /u/alpthn (Machine Learning) on April 21, 2024 at 11:40 pm

    I could've sworn I've come across a paper that proposed such a dataset, but I can't seem to find it. They basically assemble a large number of small (relative to training data) text documents, each representative of some domain .e.g., social media, academic papers, etc. The purpose is to quickly compare LMs (using the same tokenization) by measuring their perplexity on these domains. The closest thing I've found is the Pile which breaks down to 21 domains, but i'd really like to re-find this dataset. Thanks in advance! submitted by /u/alpthn [link] [comments]

  • [D] Why isn't GNN in high demand in industry?
    by /u/Snoo_72181 (Machine Learning) on April 21, 2024 at 11:04 pm

    Almost no job posting for Data Scientist or ML Engineer needs GNNs. Is it because it's computationally expensive - both time and space? Or is it because preprocessing data to a graph format is not always intuitive? Or GNN awareness is still low outside of academia? submitted by /u/Snoo_72181 [link] [comments]

  • Best AudioBooks?[D]
    by /u/ResidentMaize2535 (Machine Learning) on April 21, 2024 at 6:38 pm

    Best up to date and current books to learn machine learning and AI from a technical perspective? I work in tech but would like to further my understanding. I have a general understanding of the concept. I do a lot of driving so this is a passive listen. submitted by /u/ResidentMaize2535 [link] [comments]

  • [Research] A visual deep dive into Tesla’s data engine as pioneered by Andrej Karpathy.
    by /u/ml_a_day (Machine Learning) on April 21, 2024 at 6:18 pm

    TL;DR: Tesla uses lightweight "trigger classifiers" to detect rare scenarios when their ML model underperforms. Relevant data is uploaded to a server to improve the model, which is then trained again to cover different failure modes. How Tesla Continuously and Automatically Improves Autopilot and Full Self-Driving Capability On 5M+ Cars. A visual guide: How Tesla sets up their iterative ML pipeline P.S.: I spent several hours researching and preparing a visual deep dive of Tesla’s data engine as pioneered by Andrej Karpathy. The post lays out the iterative recipe of how Tesla improves it's fully self-driving and Autopilot capabilities. https://preview.redd.it/qxmjeavmjvvc1.jpg?width=1456&format=pjpg&auto=webp&s=94cb35f71f7e57b6bcc6e0bf9f1d5f05b5c7f086 https://preview.redd.it/htz4p8vmjvvc1.jpg?width=1456&format=pjpg&auto=webp&s=a722604b59d2c6fbb8f7e605ad496bede05a238e submitted by /u/ml_a_day [link] [comments]

Pass the 2023 AWS Cloud Practitioner CCP CLF-C02 Certification with flying colors Ace the 2023 AWS Solutions Architect Associate SAA-C03 Exam with Confidence Pass the 2023 AWS Certified Machine Learning Specialty MLS-C01 Exam with Flying Colors

List of Freely available programming books - What is the single most influential book every Programmers should read



#BlackOwned #BlackEntrepreneurs #BlackBuniness #AWSCertified #AWSCloudPractitioner #AWSCertification #AWSCLFC02 #CloudComputing #AWSStudyGuide #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AWSBasics #AWSCertified #AWSMachineLearning #AWSCertification #AWSSpecialty #MachineLearning #AWSStudyGuide #CloudComputing #DataScience #AWSCertified #AWSSolutionsArchitect #AWSArchitectAssociate #AWSCertification #AWSStudyGuide #CloudComputing #AWSArchitecture #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AzureFundamentals #AZ900 #MicrosoftAzure #ITCertification #CertificationPrep #StudyMaterials #TechLearning #MicrosoftCertified #AzureCertification #TechBooks

Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic

Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.

Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.

Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.

Turn your dream into reality with Google Workspace: It’s free for the first 14 days.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes:
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6 96DRHDRA9J7GTN6
63F733CLLY7R7MM
63F7D7CPD9XXUVT
63FLKQHWV3AEEE6
63JGLWWK36CP7WM
63KKR9EULQRR7VE
63KNY4N7VHCUA9R
63LDXXFYU6VXDG9
63MGNRCKXURAYWC
63NGNDVVXJP4N99
63P4G3ELRPADKQU
With Google Workspace, Get custom email @yourcompany, Work from anywhere; Easily scale up or down
Google gives you the tools you need to run your business like a pro. Set up custom email, share files securely online, video chat from any device, and more.
Google Workspace provides a platform, a common ground, for all our internal teams and operations to collaboratively support our primary business goal, which is to deliver quality information to our readers quickly.
Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE
C37HCAQRVR7JTFK
C3AE76E7WATCTL9
C3C3RGUF9VW6LXE
C3D9LD4L736CALC
C3EQXV674DQ6PXP
C3G9M3JEHXM3XC7
C3GGR3H4TRHUD7L
C3LVUVC3LHKUEQK
C3PVGM4CHHPMWLE
C3QHQ763LWGTW4C
Even if you’re small, you want people to see you as a professional business. If you’re still growing, you need the building blocks to get you where you want to be. I’ve learned so much about business through Google Workspace—I can’t imagine working without it.
(Email us for more codes)

error: Content is protected !!