HN Books @HNBooksMonth

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

⬐ emurillo510

hey neel8986, I know linear algebra is very important for large scale calculations. But how much calculus and statistics do you need for ML? Also, if you can touch what applications of calculus and statistics are used in ML that would be awesome :]. THANKS!

⬐ neel8986

Regarding calculus, I think basic multivariable calculus can be enough for starting. If you need a refresher you can look for (https://ocw.mit.edu/courses/mathematics/18-02sc-multivariabl...)

Also the basic idea of chain rule is important for deep learning.

Regarding statistics, I already mentioned the probability course which describes most of the important statistics concept you need. Also, some idea of Hypothesis testing can be helpful

⬐ emurillo510

right on thanks neel8986.

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/...

⬐ achompas

So this is a common misconception about the text (and Prof. Mohri's NYU class). In this case, "foundations" does not mean this is an introductory course.

Rather, the class and text provide mathematical foundations for understanding the error bounds and growth complexity of various learning algorithms. So you'll be workin with convex optimization, reproducing kernel Hilbert spaces, and Rademacher complexity--definitely not "introductory" in the least!

It's a completely different beast from Mitchell, Bishop, or EoSL (which I'm studying right now!), so I'm not sure comparisons are valid. It also fills a prominent gap in the ideas reviewed by the popular ML texts.

⬐ manaskarekar

I have been putting off buying that book because of the price, maybe I should check the library.

⬐ exg

The Elements of Statistical Learning, by T. Hastie, R. Tibshirani and J. Friedman [1] is also a very good one. Plus, the book is freely available on the authors' website.

[1] http://www-stat.stanford.edu/~tibs/ElemStatLearn/