Where to begin

So are you exited about all the news about machine learning you’ve heard lately? I sure was a year ago, and it does not take too much times to find a significant amount of information to start with. Fortunately now there is a lot to start so I wanted to make a list of videos, articles, books that I’ve found to be incredible useful to start with machine learning.

[I will keep updating this list once I remember others]

Shorts and cool

Machine Learning | El Mahdi El Mhamdi and Lê Nguyên Hoang

I came across this incredible guy explaining a lot of machine learning concepts in videos as shorts as 10 minutes, which is impressive. I am still surprise it does not have more subscriber but is just a matter of time.

Andrew Ng’s introduction to machine learning

A classic among classic, this course is a perfect balance to awake the interest on machine learning, it is not incredibly deep in math but it does not at all ignore it. Concepts like linear models, regularization, logistic regression, and neural network are covered and true be told a lot of use start

A bit longer, Lectures

We all love coursera and udacity courses, they are indeed very handy for grabbing a quick concepts, but more often than not there lack some very important mathematical proof that deepens understanding of our knowledge, here is a list of the one I consider the most useful:

University at Buffalo

John Hopkins University

Math 301 Washington University

University British Columbia

Google Machine Learning Crash Course

Statistical Machine Learning - Carnegie Mellon

Books to read

Pattern recognition and machine learning - Christopher M. Bishop [Currently reading]

Personally I’ve found this book incredibly useful to have a insight into many Statistical learning concepts, Bishop here takes a very Bayesian approach, proof and related multiple concepts. This is not a book that probably I will finish reading but instead I used it as a good reference book to clarify or refresh concepts.

Necronet