The Art of Machine Learning: A Hands-On Guide to Machine Learning with R
- 4h 6m
- Norman Matloff
- No Starch Press
- 2024
Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You’ll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbors method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You’ll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you’ll need in practice. Additional features:
- How to avoid common problems, such as dealing with “dirty” data and factor variables with large numbers of levels
- A look at typical misconceptions, such as dealing with unbalanced data
- Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method
- Dozens of illustrative examples involving real datasets of varying size and field of application
- Standard R packages are used throughout, with a simple wrapper interface to provide convenient access.
After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.
About the Author
Norman Matloff is an award-winning professor at the University of California, Davis. Matloff has a PhD in mathematics from UCLA and is the author of The Art of Debugging with GDB, DDD, and Eclipse and The Art of R Programming (both from No Starch Press).
In this Book
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Introduction
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Regression Models
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Classification Models
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Bias, Variance, Overfitting, and Cross-Validation
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Dealing with Large Numbers of Features
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A Step Beyond k-NN: Decision Trees
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Tweaking the Trees
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Finding a Good Set of Hyperparameters
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Parametric Methods
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Cutting Things Down to Size: Regularization
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A Boundary Approach: Support Vector Machines
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Linear Models on Steroids: Neural Networks
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Image Classification
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Handling Time Series and Text Data
