*Are you wondering whether to get into the ‘R’ bus or ‘Python’ bus?*

*My suggestion is to you is “Why not get into the ‘R and Python’ train?”*

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 ($8.99/Rs449) versions. In the third edition all code sections have been re-formatted to use the fixed width font ‘Consolas’. This neatly organizes output which have columns like confusion matrix, dataframes etc to be columnar, making the code more readable. There is a science to formatting too!! which improves the look and feel. It is little wonder that Steve Jobs had a keen passion for calligraphy! Additionally some typos have been fixed.

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 Python Third Edition – Machine Learning in Stereo(Kindle- $8.99/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!

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Can you point to any positive reviews from objective, reputable sources? The only reviews I see (about the 2nd edition of the book) are negative but I don’t find them to be compelling.

SR-You can check the comments on my blog posts. Then there are the reviews on Amazon which you go through

All I can say is that it took a lot of thought, effort and the completion of several courses to write this book.

I am proud of the outcome! I am not compelling anyone to buy. Make your own reasoned judgment!

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