Machine Learning with R: Expert techniques for predictive modeling, 3 edition by Brett Lantz
- Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond
- Harness the power of R to build flexible, effective, and transparent machine learning models
- Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz
Book DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.
Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.
This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
What you will learn
- Discover the origins of machine learning and how exactly a computer learns by example
- Prepare your data for machine learning work with the R programming language
- Classify important outcomes using nearest neighbor and Bayesian methods
- Predict future events using decision trees, rules, and support vector machines
- Forecast numeric data and estimate financial values using regression methods
- Model complex processes with artificial neural networks ― the basis of deep learning
- Avoid bias in machine learning models
- Evaluate your models and improve their performance
- Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow
Who this book is forData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
2. Managing and Understanding Data
3. Lazy Learning – Classification Using Nearest Neighbors
4. Probabilistic Learning – Classification Using Naive Bayes
5. Divide and Conquer – Classification Using Decision Trees and Rules
6. Forecasting Numeric Data – Regression Methods
7. Black Box Methods – Neural Networks and Support Vector Machines
8. Finding Patterns – Market Basket Analysis Using Association Rules
9. Finding Groups of Data – Clustering with k-means
10. Evaluating Model Performance
11. Improving Model Performance
12. Specialized Machine Learning Topics