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I've been learning machine learning for the past few weeks from books and online courses. The books I've been reading, and currently still reading is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and I also read a little bit of "Essential Math for Data Science". However, my main concern is these books tend to try to explain the math, but they don't really put that much detail, so I'm confused with some parts of the implementations of the models like softmax regressions with stochastic-GD.

I would like some suggestions on where I can learn the math and the implementations well. I would love there's a single source for all this because I tend to struggle to connect the strings from multiple different sources into 1. Is there any good math for data science books? It doesn't have to contain a lot of codings.

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I guess I can answer this question well as I have been studying the intensive mathematics required for ML. People often tend to ignore the maths required for Machine Learning and directly jump to the coding part.

Here are few of my suggestions in the preference order:

  1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

  2. Pattern Recognition and Machine Learning by Christopher M. Bishop

  3. Pattern Classification by David G. Stork, Peter E. Hart, and Richard O. Duda

You can easily find the PDFs for these books online as well, or you can prefer buying them.

My top recommendation would be ESL and then PRML

ESL is a book which is often read by research scholars and you will need a great understanding of maths(linear algebra, optimization, and statistics) to understand the book well, otherwise, it might feel a little overwhelming.

Also, I am attaching a link to my Machine Learning Notes

I made these while studying Maths in Machine Learning.

These might be helpful :D

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    $\begingroup$ Oh my, what a good answer, thank you so much! :D $\endgroup$ Commented Oct 27, 2023 at 11:42

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