Three New Interesting Books on Data Science/Machine Learning

Three new practical books on Data Science/Machine Learning have got published recently. All three are introductory level books taking Hands-on approaches to teach Machine Learning and Data Science toolbox. All three books are great additions to learn Machine Learning with minimal math.

Hands-on Machine Learning with R

Hands-on Machine Learning with R by Brad Boehmke and Brandon M. Greenwell is a new book that looks really interesting for learning how to use common Machine Learning algorithms in R. Basically, the book provides hands-on guide to how to build machine learning models using a variety of algorithms and how to tune them using well-established R packages. The authors state that

Our motivation in almost every case to describe the techniques in a way that helps develop intuition behind its strengths and weaknesses. For the most part we minimize mathematical complexity when possible but also provide resources to get deeper into the details if desired.

That said this is not a book for learning R, rather a book for learning how to use Machine Learning methods using R.

Another best thing about the book is the whole book is available for free at
https://bradleyboehmke.github.io/HOML

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

The Second Edition of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron is available now. It is a practical book that shows how to use a variety of Machine Learning algorithms to learn from data. Being a Hands-On book, it gives clear examples with minimal theory to give an intuition behind concepts.

As the preface to the book describes,

You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

One of the biggest highlights is that the book shows you how to use the latest version TensorFlow 2 to build and train neural networks. Looking forward to getting my hands-on with these two books.

Introduction to Data Science: Data Analysis and Prediction Algorithms with R

Introduction to Data Science: Data Analysis and Prediction Algorithms with R is by none other than the Harvard Statistics/Data Science professor Rafael Irizarry. Introduction to Data Science is from his Data Science course at Harvard and it introduces “concepts and skills that can help you tackle real-world data analysis challenges”.

Introduction to Data Science book starts with the basics of R programming language, data analysis/visualization skills with tidyverse. And then goes on to cover probability, statistical inference, linear regression, and machine learning.

The free online book has been available for a while at https://rafalab.github.io/dsbook/ and now the physical copy of Introduction to Data Science is available.