Free Daily Dose of Data Science Archive

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

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.

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!

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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