This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up

First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any idea of what is happening inside Following concepts are very essential

1) Linear Algebra (MIT https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra... ) 2) Probability (Harvard https://www.youtube.com/watch?v=KbB0FjPg0mw )

Get some basic grasp of machine learning. Get a good intuition of basic concepts

1) Andrew Ng coursera course (https://www.coursera.org/learn/machine-learning)

2) Tom Mitchell book (https://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/00...)

Both the above course and book are super easy to follow. You will get a good idea of basic concepts but they lack in depth. Now you should move to more intense books and courses

You can get more in-depth knowledge of Machine learning from following sources

1)Nando machine learning course ( https://www.youtube.com/watch?v=w2OtwL5T1ow)

2)Bishops book (https://www.amazon.in/Pattern-Recognition-Learning-Informati...)

Especially Bishops book is really deep and covers almost all basic concepts.

Now for recent advances in Deep learning. I will suggest two brilliant courses from Stanford

1) Vision ( https://www.youtube.com/watch?v=NfnWJUyUJYU )

2) NLP ( https://www.youtube.com/watch?v=OQQ-W_63UgQ)

The Vision course by Karparthy can be a very good introduction to Deep learning. Also, the mother book for deep learning ( http://www.deeplearningbook.org/ )is good

Sidenote: I highly recommend Tom Mitchell's (the professor) book as a general introduction to the topic.

[0]: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007...

My favorite introductory textbook on machine learning is the Tom Mitchell book: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007...

The Bishop book is the most popular though: http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...