-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
Mastering Machine Learning with R
By :
Mastering Machine Learning with R
By:
Overview of this book
Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data.
The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series.
The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.
Table of Contents (15 chapters)
Preface
Free Chapter
1. A Process for Success
2. Linear Regression – The Blocking and Tackling of Machine Learning
3. Logistic Regression and Discriminant Analysis
4. Advanced Feature Selection in Linear Models
5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
6. Classification and Regression Trees
7. Neural Networks
8. Cluster Analysis
9. Principal Components Analysis
10. Market Basket Analysis and Recommendation Engines
11. Time Series and Causality
12. Text Mining
A. R Fundamentals
Index
Customer Reviews
