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I'm currently taking a course "Introduction to Machine Learning" which covers the following topics: linear regression, overfitting, classification problems, parametric & non-parametric models, Bayesian & non Bayesian models, generative classification, neural networks, SVM, boosting & bagging, unsupervised learning. I've asked the course stuff for some reading material about those subjects but I would like to hear some more recommendations about books (or any other material) that give more intuition about the listed topics to start with and also some books that go deeper into the theory of those subjects (affter I'll gain some intuition)? (I guess every subject has many books to cover the theory of course, but any recommendations will be great)

Thank you.

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For theory Tibshirani: The elements of statistical learning https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Also Andrew NG and other books from deeplearning.ai: Machine Learning Yearning https://d2wvfoqc9gyqzf.cloudfront.net/content/uploads/2018/09/Ng-MLY01-13.pdf

of course the applied machine learning books on computer languages: An introduction into statistical learning in R http://faculty.marshall.usc.edu/gareth-james/ISL/

or deep learning with python: http://faculty.neu.edu.cn/yury/AAI/Textbook/Deep%20Learning%20with%20Python.pdf

Of course there are a lot of free public pdf books in internet. However in my opinion for the beginning Tibshirani and Andrew NG are good. (and of course take your own project and ask questions on stackoverflow. That helps me a lot).

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Definately cover the bayesian side with the Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Its a classic.

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