Two interesting books to start with Machine Learning

There are a lot of books in the field of Machine Learning, just a fast search in Amazon gives you more than 25.ooo books. I wanted to filter all those books an choose the most useful. I was looking in google, quora and reading some post that I found around internet. There a lot of people giving a list of 10 – 20 books about machine learning, statistical learning, reinforcement learning… I just wanted to find the two interesting books to go into the field.

With these books, it is possible to learn general aspects about the topic and later go more in deep in the part that sounds more interesting.

 


 

Machine Learning

The “book” that everyone recommend as a good point to start, written by Tom M. Mitchell (professor in the Carnegie Mellon University).

This is an introduction book for the field. You don’t need to have previous knowledge in Machine Learning.

Some topics that you will find in the book: decision tree learning, artificial neural networks, bayesian learning, computational learning, genetic algorithms, reinforcement learning and more.

 


 

Pattern Recognition and Machine Learning (Information Science and Statistics)

The author is Christopher M. Bishop, a Distinguished Scientist at Microsoft Research Cambridge, where he leads the Machine Learning and Perception group

This book will give you a really good approach to the commonly used algorithms in Machine Learning.

 


 

Both books are theoretical and will give you a good introduction. Of course there so many books in the area, some of then more practical, some about statistical learning… But I think it is good to have a simple point to start.

I have started with Tom M. Mitchell’s book. I will give you my impression when I have finished it.