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This question already has an answer here:

I'm just starting off as a data scientist and i need to understand how regression, random forest and much more.. algorithms function under the hood, the maths behind them and when to use them. I started with some MOOC courses but not a fan, it's just not my style of learning as i need practical examples or books i can dive into with detailed examples i can just apply myself after understanding how each algorithm works and some 20 mins vids with scarce maths explanation is not what i aim for, i've done some research and ended up with these books:

-python for data analysis

-Python Crash Course

-oreilly hands on machine learning with scikit learn and tensorflow

-automate the boring stuff with python

kindly note that i have some python experience(less than a year) and unfamiliar with tensorflow , keras, scikit-learn etc..

Kindly also note that i didn't mention any maths books to explain how the machine learning algorithms work so i'm open to suggestions .

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marked as duplicate by Toros91, Stephen Rauch, Aditya, Icyblade, oW_ Jun 1 '18 at 16:03

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

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If you want to get more into Machine Learning and the mathematical/theoritical aspects that it includes, the book mentioned below is an option:

Machine Learning by Tom Mitchell, McGraw Hill, 1997.

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Regarding Deep Learning (as sub-part of Machine Learning), the best resource is:

http://www.deeplearningbook.org/

It also has a very good recap of the mathematic background required to understand the theory.

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