I'm just starting off as a data scientist and i need to understand how regression,loss functions, overfitting, PCA,Clustering: k-means, 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 .


Take a look at the GitHub repositories listed below. They provide a very comprehensible list of available material related to ML and pick a course/book most related to what you are trying to learn.

https://github.com/josephmisiti/awesome-machine-learning https://github.com/ChristosChristofidis/awesome-deep-learning https://github.com/aikorea/awesome-rl

I think you will have to go through multiple courses and books to learn everything you listed.

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