# Which Machine Learning book to choose (APM, MLAP or ISL)? [closed]

I'm searching a book as a refresher in machine learning (I have taken a lecture in machine learning sometime ago). I will be applying machine learning in a project.

I have searched a lot of books and found the following three the one which best fits me:

• Applied Predictive Modeling by Kuhn and Johnson
• Machine learning an Algorithmic Perspective (second edition) by Marsland
• An Introduction to Statistical Learning by Hastie

Applied Predictive Modeling and An Introduction to Statistical Learning uses R as examples and Machine learning an Algorithmic Perspective uses Python. The used language does not matter that much because I'm not really interested in language examples (I'm using Matlab) and I will probably skip such examples.

The advantage of Applied Predictive Modeling seems to be that it covers the whole machine learning procedure (feature selection etc.) and seems to be very well written. The advantage of Machine learning an Algorithmic Perspective (second edition) is that it covers more topics (ensemble learning, graphical models, gaussian processes) and has perhaps a bit more math.

Which one of these three books would you recommend (and why)?

It is like a bible for all those who want to get started at statistical analysis and Machine Learning. It gives an excellent intuition about the theory behind all those models and algorithms, enough to pursue research and/or dive into another advanced ML book like the Applied Predictive Modeling by Kuhn and Johnson