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(This article was first published on <strong><a href="https://gigadom.wordpress.com/2017/12/05/my-book-practical-machine-learning-with-r-and-python-on-amazon/"> R – Giga thoughts …</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><img data-orig-file="https://gigadom.files.wordpress.com/2017/12/untitled.png" data-orig-size="1267,451" data-medium-file="https://gigadom.files.wordpress.com/2017/12/untitled.png?w=300" data-attachment-id="5891" data-image-meta="{" src="https://gigadom.files.wordpress.com/2017/12/untitled.png?w=450&h=238&fit=668%2C238" data-image-description="" data-permalink="https://gigadom.wordpress.com/2017/12/05/my-book-practical-machine-learning-with-r-and-python-on-amazon/untitled-111/" data-large-file="https://gigadom.files.wordpress.com/2017/12/untitled.png?w=450&amp;h=238&amp;fit=668%2C238" alt="" data-image-title="Untitled" sizes="(max-width: 668px) 100vw, 668px" class="wp-image-5891" data-comments-opened="1" data-recalc-dims="1">My book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($9.99) and kindle ($6.97/Rs449) versions. In this book I implement some of the most common, but important Machine Learning algorithms in R and equivalent Python code. This is almost like listening to parallel channels of music in stereo!<br>
1. <a rel="nofollow" href="https://www.amazon.com/dp/1973443503" target="_blank">Practical machine with R and Python – Machine Learning in Stereo</a> (Paperback)<br>
2. <a rel="nofollow" href="https://www.amazon.in/dp/B077WFS87Z" target="_blank">Practical machine with R and Python – Machine Learning in Stereo</a> (Kindle)<br>
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.</p>
<p>Here is a look at the topics covered</p>
<p><strong>Table of Contents</strong><br>
Essential R …………………………………….. 7<br>
Essential Python for Datascience ………………..   54<br>
R vs Python ……………………………………. 77<br>
Regression of a continuous variable ………………. 96<br>
Classification and Cross Validation ……………….113<br>
Regression techniques and regularization …………. 134<br>
SVMs, Decision Trees and Validation curves …………175<br>
Splines, GAMs, Random Forests and Boosting …………202<br>
PCA, K-Means and Hierarchical Clustering …………. 234</p>
<p>Pick up your copy today!!<br>
Hope you have a great time learning as I did while implementing these algorithms!</p>
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