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
