Elements of Statistical Learning alternatives Elements of Statistical Learning (ESL) is a book that has fantastic breadth and depth. It covers the essentials to the very modern methods by citing the papers where these original studies come about. However, I really find the language of the book very very prohibitive. I believe there is an easier way to discuss concepts. I find ESL simply too overwhelming. Can someone suggest alternatives that are friendlier to the uninitiated? 
I found the sibling to ESL: Introduction to Statistical Learning. That is tone I want to read and understand. It is accommodating, without dumbing things down. Any thing similar to Intro to SL? 
 A: I agree that An Intro to Statistical Learning has a very accommodating tone. You may want to look at Learning From Data, A Short Course by Yaser Abu-Mostafa et al. I found this book and the accompanying youtube videos to be great. 
Lastly, spdrnl's comment about Applied Predictive Modeling by Kuhn is a good suggestion. I have not read it yet, but I have perused it and it seems like a great resource as well.
A: Possible alternatives:


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*Pattern Recognition and Machine Learning by Christopher Bishop: I don't like the book's notation systems, but I heard the graphical model chapter is good

*Machine Learning: A Probabilistic Perspective by Kevin P. Murphy:
like a dictionary, describe various of pre-deep-learning-era machine learning methods 

*Deep Learning Book: Newer, covering more about deep learning part 

*Dive into Deep Learning: Possibly newest deep learning book so far
Also, try some course notes:


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*CS229T: Statistical Learning Theory

*CS 228: Probabilistic Graphical Models

*CS 236: Deep Generative Models
