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 ContentsPreface …………………………………………………………………………….4Introduction ………………………………………………………………………61. Essential R ………………………………………………………………… 82. Essential Python for Datascience ……………………………………………573. R vs Python …………………………………………………………………814. Regression of a continuous variable ……………………………………….1015. Classification and Cross Validation ………………………………………..1216. Regression techniques and regularization ………………………………….1467. SVMs, Decision Trees and Validation curves ………………………………1918. Splines, GAMs, Random Forests and Boosting ……………………………2229. PCA, K-Means and Hierarchical Clustering ………………………………258References ……………………………………………………………………..269
Pick up your copy today!!Hope you have a great time learning as I did while implementing these algorithms!
Related