#### Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Title Page
Credits
Packt Upsell
Customer Feedback
Preface
Free Chapter
A Process for Success
Linear Regression - The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks and Deep Learning
Cluster Analysis
Principal Components Analysis
Market Basket Analysis, Recommendation Engines, and Sequential Analysis
Creating Ensembles and Multiclass Classification
Time Series and Causality
Text Mining
R on the Cloud
R Fundamentals
Sources

## An overview of the principal components

PCA is the process of finding the principal components. What exactly are these?

We can consider that a component is a normalized linear combination of the features (James, 2012). The first principal component in a dataset is the linear combination that captures the maximum variance in the data. A second component is created by selecting another linear combination that maximizes the variance with the constraint that its direction is perpendicular to the first component. The subsequent components (equal to the number of variables) would follow this same rule.

A couple of things here. This definition describes the linear combination, which is one of the key assumptions in PCA. If you ever try and apply PCA to a dataset of variables having a low correlation, you will likely end up with a meaningless analysis. Another key assumption is that the mean and variance for a variable are sufficient statistics. What this tells us is that the data should fit a normal...