**Note**: The 3rd edition of this book is now available My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon

The third edition of my book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($12.99) and kindle ($9.99/Rs449) versions. This second edition includes more content, extensive comments and formatting for better readability.

In this book I implement some of the most common, but important Machine Learning algorithms in R and equivalent Python code.

1. Practical machine with R and Python: Third Edition – Machine Learning in Stereo(Paperback-$12.99)

2. Practical machine with R and Third Edition – Machine Learning in Stereo(Kindle- $9.99/Rs449)

This book is ideal both for beginners and the experts in R and/or Python. Those starting their journey into datascience and ML will find the first 3 chapters useful, as they touch upon the most important programming constructs in R and Python and also deal with equivalent statements in R and Python. Those who are expert in either of the languages, R or Python, will find the equivalent code ideal for brushing up on the other language. And finally,those who are proficient in both languages, can use the R and Python implementations to internalize the ML algorithms better.

Here is a look at the topics covered

Table of Contents

Preface …………………………………………………………………………….4

Introduction ………………………………………………………………………6

1. Essential R ………………………………………………………………… 8

2. Essential Python for Datascience ……………………………………………57

3. R vs Python …………………………………………………………………81

4. Regression of a continuous variable ……………………………………….101

5. Classification and Cross Validation ………………………………………..121

6. Regression techniques and regularization ………………………………….146

7. SVMs, Decision Trees and Validation curves ………………………………191

8. Splines, GAMs, Random Forests and Boosting ……………………………222

9. PCA, K-Means and Hierarchical Clustering ………………………………258

References ……………………………………………………………………..269

Pick up your copy today!!

Hope you have a great time learning as I did while implementing these algorithms!