Practical Machine Learning with R and Python: Machine Learning in Stereo

✍ Scribed by Tinniam V Ganesh

• Publisher: Independently published
• Year: 2017
• Tongue: English
• Leaves: 775
• Category: Library

✦ Synopsis

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 chapters 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 implementations 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
Introduction
1.Logistic Regression as a Neural Network
2.Implementing a simple Neural Network
3.Building a L- Layer Deep Learning Network
4.Deep Learning network with the Softmax
5.MNIST classification with Softmax
6.Initialization, regularization in Deep Learning
7.Gradient Descent Optimization techniques
8.Gradient Check in Deep Learning
1.Appendix A
2.Appendix 1 – Logistic Regression as a Neural Network
3.Appendix 2 - Implementing a simple Neural Network
4.Appendix 3 - Building a L- Layer Deep Learning Network
5.Appendix 4 - Deep Learning network with the Softmax
6.Appendix 5 - MNIST classification with Softmax
7.Appendix 6 - Initialization, regularization in Deep Learning
8.Appendix 7 - Gradient Descent Optimization techniques
9.Appendix 8 – Gradient Check
References