Free Daily Dose of Data Science Archive

Collection of posts on core DS/ML topics.

Today, I am releasing the 2024 edition of the Daily Dose of Data Science archive. Download it below:

Daily Dose Of Data Science Full Archive

88.3MB ∙ PDF file

Download

Download

The reading time of this book is about 9-10 hours. But not all chapters will be of relevance to you.

This 2-minute assessment will test your current expertise and recommend chapters that will be most useful to you.

Click this link or the button below to start the assessment.

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What’s different in this version of the book?

• The older version was quite random and far from what you call a “book.”

  • In this version, I have categorized posts based on topics to easily navigate the book and enhance the reading experience.

  • Moreover, I have put extra effort into connecting different posts and ensuring a genuine flow while reading the book

• The older version made me realize that not all chapters are relevant to everyone.

  • This time, a 2-minute self-assessment is available in the book, which will suggest the most relevant chapters for you.

• The entire book is dedicated to no-fluff and core data science and machine learning technical topics, with code wherever possible.

  • The objective is to save you as much time as possible while maximizing the value you get.

  • So, in this version, I have removed many basic posts that did not add much value, in my opinion.

  • Once you finish reading, you will end up with a 10x better skillset.

I would love to have your feedback on this new edition :)

Thanks for reading!

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Are you overwhelmed with the amount of information in ML/DS?

Every week, I publish no-fluff deep dives on topics that truly matter to your skills for ML/DS roles.

For instance:

• A Crash Course on Causality – Part 1

• A Crash Course on Causality – Part 2

• A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KANs)

• Implementing KANs From Scratch Using PyTorch

• A Practical Guide to Scaling ML Model Training

• 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• A Beginner-Friendly Guide to Multi-GPU Model Training

• Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning

• 8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science

• Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming

• You Are Probably Building Inconsistent Classification Models Without Even Realizing

• And many many more.

Join below to unlock all full articles:

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