Easier than Element of statistical Learning and harder than Introduction to statistical learning I'm majoring industrial engineering on a master's course. Recently, I've realized that I need to study statistical perspective on M.L.
So I'm studying the book Introduction to statistical learning with myself. But it seems to lack of mathematical background. On the other hand, Element of statistical learning is too hard for me so I barely understand it.
Can you have any recommendation just middle of ESL and ISLR?: which includes richly and kindly explanations on math and statistics?
 A: I like Learning From Data by Abu-Mustafa, et al., which should be enjoyed with the You-Tubed lecture series. It accurately describes itself as a short course, but not a hurried course. Neural nets get half of one lecture, which makes perfect sense when you get there. 
ESL is a long book and a hurried book. It is best if you already know all of the fundamentals. I think Boyd and Vanderberghe Convex Optimization and Blitzstein and Hwang Intro to Probability and Lay Linear Algebra are important prerequisites for ESL. Also Casella and Berger, because we all come from Statistical Inference.
A: It seems that Chapman & Hall's Machine learning book should suit your need. From the description: "Traditional books on machine learning can be divided into two groups ― those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text."
