Chapter 1: Getting Started with R and Machine Learning

Delving into the basics of R

Chapter 2: Let's Help Machines Learn

Understanding machine learning

Algorithms in machine learning

Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis

Detecting and predicting trends

Evaluating a product contingency matrix

Frequent itemset generation

Chapter 4: Building a Product Recommendation System

Understanding recommendation systems

Issues with recommendation systems

Building a recommender engine

Production ready recommender engines

Chapter 5: Credit Risk Detection and Prediction – Descriptive Analytics

Data analysis and transformation

Chapter 6: Credit Risk Detection and Prediction – Predictive Analytics

How to predict credit risk

Important concepts in predictive modeling

Modeling using logistic regression

Modeling using support vector machines

Modeling using decision trees

Modeling using random forests

Modeling using neural networks

Model comparison and selection

Chapter 7: Social Media Analysis – Analyzing Twitter Data

Social networks (Twitter)

Data mining @social networks

Getting started with Twitter APIs

Challenges with social network data mining

Chapter 8: Sentiment Analysis of Twitter Data

Understanding Sentiment Analysis

Sentiment analysis upon Tweets

Chapter 9: Introducing Machine Learning

The origins of machine learning

Uses and abuses of machine learning

Machine learning in practice

Chapter 10: Managing and Understanding Data

Exploring and understanding data

Chapter 11: Lazy Learning – Classification Using Nearest Neighbors

Understanding nearest neighbor classification

Example – diagnosing breast cancer with the k-NN algorithm

Chapter 12: Probabilistic Learning – Classification Using Naive Bayes

Understanding Naive Bayes

Example – filtering mobile phone spam with the Naive Bayes algorithm

Chapter 13: Divide and Conquer – Classification Using Decision Trees and Rules

Understanding decision trees

Example – identifying risky bank loans using C5.0 decision trees

Understanding classification rules

Example – identifying poisonous mushrooms with rule learners

Chapter 14: Forecasting Numeric Data – Regression Methods

Example – predicting medical expenses using linear regression

Understanding regression trees and model trees

Example – estimating the quality of wines with regression trees and model trees

Chapter 15: Black Box Methods – Neural Networks and Support Vector Machines

Understanding neural networks

Example – Modeling the strength of concrete with ANNs

Understanding Support Vector Machines

Example – performing OCR with SVMs

Chapter 16: Finding Patterns – Market Basket Analysis Using Association Rules

Understanding association rules

Example – identifying frequently purchased groceries with association rules

Chapter 17: Finding Groups of Data – Clustering with k-means

Example – finding teen market segments using k-means clustering

Chapter 18: Evaluating Model Performance

Measuring performance for classification

Estimating future performance

Chapter 19: Improving Model Performance

Tuning stock models for better performance

Improving model performance with meta-learning

Chapter 20: Specialized Machine Learning Topics

Working with proprietary files and databases

Working with online data and services

Working with domain-specific data

Improving the performance of R

Chapter 21: A Process for Success

Chapter 22: Linear Regression – The Blocking and Tackling of Machine Learning

Univariate linear regression

Multivariate linear regression

Other linear model considerations

Chapter 23: Logistic Regression and Discriminant Analysis

Classification methods and linear regression

Chapter 24: Advanced Feature Selection in Linear Models

Regularization in a nutshell

Chapter 25: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines

Feature selection for SVMs

Chapter 26: Classification and Regression Trees

An overview of the techniques

Chapter 27: Neural Networks

Deep learning, a not-so-deep overview

Data understanding and preparation

An example of deep learning

Chapter 28: Cluster Analysis

Gower and partitioning around medoids

Data understanding and preparation

Chapter 29: Principal Components Analysis

An overview of the principal components

Chapter 30: Market Basket Analysis and Recommendation Engines

An overview of a market basket analysis

Data understanding and preparation

An overview of a recommendation engine

Business understanding and recommendations

Data understanding, preparation, and recommendations

Modeling, evaluation, and recommendations

Chapter 31: Time Series and Causality

Univariate time series analysis

Chapter 32: Text Mining

Text mining framework and methods