Books recommendation series: Elements of statistical learning

Since long I was thinking to write down my recommendation of the books I have read recently or in past as well. The plan is to post atleast one book recommendation weekly.

Check below my first recommendation.

Book

Elements of statistical learning
Hastie, Tibshirani, and Friedman (2009 )
https://web.stanford.edu/~hastie/ElemStatLearn/

Theme

During the past decade has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting–the first comprehensive treatment of this topic in any book. 

Takeaway

It is a rigorous and mathematically dense book on machine learning techniques. It gives a very good explanation of how the correlation between Bias, Variance and Model Complexity works. If you have the mathematical background (calculus, linear algebra etc) this is a very good introduction to Machine Learning and covers most of the MI topics. I can say that it has a nice balance between mathematical concepts and intuitive reasoning.
I highly recommend this book for anyone entering to the field of AI/ML.

Suggestions

  • What this book doesn’t provide? is a pragmatic approach or Hands-on practice.
  • Deep analysis of why a specific method works (but it gives you some intuition about what a method is trying to do)
  • If you are doing self-study and don’t have any background in machine learning or statistics advice to refine your understanding of linear algebra and calculus before reading this book.
  • Free PDF is available but suggest to buy a print book.

Download and purchase link

Buy: https://web.stanford.edu/~hastie/ElemStatLearn/
PDF: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf