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(This article was first published on <strong><a href="https://gigadom.wordpress.com/2018/06/01/my-book-practical-machine-learning-in-r-and-python-second-edition-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>The second edition of my book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both <a rel="nofollow" href="https://www.amazon.com/dp/1983035661" target="_blank">paperback</a> ($10.99) and <a rel="nofollow" href="https://www.amazon.com/dp/B07DFKSCWZ" target="_blank">kindle</a> ($7.99/Rs449) versions.  This second edition includes more content,  extensive comments and formatting for better readability.</p>
<p><img data-orig-file="https://gigadom.files.wordpress.com/2018/06/untitled.png" data-orig-size="1275,546" data-medium-file="https://gigadom.files.wordpress.com/2018/06/untitled.png?w=300" data-attachment-id="6828" data-image-meta="{" data-image-description="" src="https://gigadom.files.wordpress.com/2018/06/untitled.png?w=450&h=289&fit=676%2C289" data-permalink="https://gigadom.wordpress.com/2018/06/01/my-book-practical-machine-learning-in-r-and-python-second-edition-on-amazon/untitled-125/" data-large-file="https://gigadom.files.wordpress.com/2018/06/untitled.png?w=450&amp;h=289&amp;fit=676%2C289" alt="" data-image-title="Untitled" sizes="(max-width: 676px) 100vw, 676px" data-comments-opened="1" class="wp-image-6828" data-recalc-dims="1">In this book I implement some of the most common, but important Machine Learning algorithms in R and equivalent Python code.<br>
1. <a rel="nofollow" href="https://www.amazon.com/dp/1983035661" target="_blank">Practical machine with R and Python: Second Edition – Machine Learning in Stereo</a>(Paperback-$10.99)<br>
2. <a rel="nofollow" href="https://www.amazon.com/dp/B07DFKSCWZ" target="_blank">Practical machine with R and Python Second Edition – Machine Learning in Stereo</a>(Kindle- $7.99/Rs449)</p>
<p>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>Table of Contents<br>
Preface …………………………………………………………………………….4<br>
Introduction ………………………………………………………………………6<br>
1. Essential R ………………………………………………………………… 8<br>
2. Essential Python for Datascience ……………………………………………57<br>
3. R vs Python …………………………………………………………………81<br>
4. Regression of a continuous variable ……………………………………….101<br>
5. Classification and Cross Validation ………………………………………..121<br>
6. Regression techniques and regularization ………………………………….146<br>
7. SVMs, Decision Trees and Validation curves ………………………………191<br>
8. Splines, GAMs, Random Forests and Boosting ……………………………222<br>
9. PCA, K-Means and Hierarchical Clustering ………………………………258<br>
References ……………………………………………………………………..269</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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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://gigadom.wordpress.com/2018/06/01/my-book-practical-machine-learning-in-r-and-python-second-edition-on-amazon/"> R – Giga thoughts …</a></strong>.</div>
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