PracticalMachineLearningWithRandPython

Machine Learning in Stereo

This book is now available on Amazon in Paperback and Kindle versions

• Practical machine Learning with Python- Machine Learning in Stereo
• Practical machine Learning with Python- Machine Learning in Stereo

This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.

The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence. those with no knowledge of R and Python will find these introductory chapter useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implements useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python

Table of Contents Preface 4

1. Essential R ___________________________________________________ 7
2. Essential Python for Datascience ____________________________ 54
3. R vs Python ____________________________________________________77
4. Regression of a continuous variable ___________________________ 96
5. Classification and Cross Validation __________________________113
6. Regression techniques and regularization ______________________134
7. SVMs, Decision Trees and Validation curves ___________________ 175
8. Splines, GAMs, Random Forests and Boosting ___________________ 202
9. PCA, K-Means and Hierarchical Clustering _____________________ 234 References _______________________________________________________ 244