Looking for Recommended Texts to Complement Supervised Machine Learning Course

Course Q&A Machine Learning Specialization Supervised ML: Regression and Classification
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Hi,

I have just completed the Supervised Machine Learning: Regression and Classification course, and I really enjoyed it. It exceeded my expectations.

It would be useful if there were an accompanying text or primer that I could keep on hand for future reference.

What I found missing from the excellent course were summaries of what was learned and how things fit together. I would like to fill that gap myself with a text I can use for later reference.

Any suggested texts that cover similar topics (and ideally use the same notations) would be appreciated!

Thank you!
Paul

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I’m glad you enjoyed the Supervised Machine Learning course! Here are some texts that fit well with the topics covered in the course:

  1. “Pattern Recognition and Machine Learning” by Christopher M. Bishop
    This is a thorough and mathematically rigorous book on supervised learning, regression, and classification. It’s an excellent resource for anyone looking to deepen their understanding.

  2. “Machine Learning Yearning” by Andrew Ng
    This free book is an excellent companion to the specialization, providing insights into how to structure machine learning projects and make decisions, but with a more practical focus than mathematical. While not exhaustive, it complements the course nicely.

  3. “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
    This book provides an extensive overview of machine learning methods, including linear regression, classification, and more. It’s widely recommended in academic and professional circles and uses a notation style similar to the course.

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Congratulations !

“The Elements of Statistical Learning” is an excellent book, though it’s more of graduate level textbook. To really understand the material you would need Multivariate Calculus, Linear Algebra, and a decent 1 year sequence of a Probability/Statistics course. I love the book. But for an easier introduction, check out “Introduction to Statistical Learning”. Written by the same awesome author. It has labs at the end of chapters, and it was initially based on R, but the last edition also has a Python version.

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Apart from all the recommended text. I personally use the following resources.

  1. Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi and Vahis. (To go deeper into the practical aspect)

  2. Mathematics for Machine Learning by Marc Peter, Cheng Soon Ong and Also Faisai (To go deeper into the mathematics)

  3. The PML Introduction book by Kelvin Murphy (A tough book to read that go into the mathematical aspect of Machine Learning and DeepLearning). Need a strong foundation in Maths.

  4. Take Andrew Ng DeepLearning Specialization as a follow up.

Have fun

Regards

Alan

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Hi @chuaal @nadtriana @Nick_B1 You have all given me an abundance of suggestions. I have already been spending some my time looking at each of them. Eventually, I want to update the post with what I learn. But that may take me a while, so I will thank you now for taking the time. I am genuinely grateful.
Thank you!
Paul