Mastering Machine Learning with R – Second Edition

With Mastering Machine Learning with R – Second Edition, understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning. Implement advanced concepts in machine learning with this example-rich guide.(Limited-time offer)

Table of Contents

• 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

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