Key Features
Book Description
What you will learn
- Harness the power of R to build common machine learning algorithms with realworld data science applications
- Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results
- Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems
- Classify your data with Bayesian and nearest neighbour methods
- Predict values using R to build decision trees, rules, and support vector machines
- Forecast numeric values with linear regression and model your data with neural networks
- Evaluate and improve the performance of machine learning models
- Learn specialized machine learning techniques for text mining, social network data, and big data
Who this book is for
Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Table of Contents
- Introducing Machine Learning
- Managing and Understanding Data
- Lazy Learning: Classification using Nearest Neighbors
- Probabilistic Learning: Classification using Naïve Bayes
- Divide and Conquer: Classification using Trees and Rules
- Forecasting Numeric Data: Regression Methods
- Black Box Methods: Neural Networks and Support Vector Machines
- Finding Patterns: Market Basket Analysis Using Association Rules
- Finding Groups of Data: Clustering with k-means
- Evaluating Model Performance
- Improving Model Performance
- Specialized Machine Learning Topics
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