Название: The Art of Machine Learning: A Hands-On Guide to Machine Learning with RАвтор: Norman Matloff
Издательство: No Starch Press
Год: 2024
Страниц: 272
Язык: английский
Формат: epub (true), mobi
Размер: 22.9 MB
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.
Machine Learning (ML) is all about prediction. Does a patient have a certain disease? Will a customer switch from her current cell phone service to another? What is actually being said in this rather garbled audio recording? Is that bright spot observed by a satellite a forest fire or just a reflection?
We predict an outcome from one or more features. In the disease diagnosis example, the outcome is having the disease or not, and the features may be blood tests, family history, and so on. All ML methods involve a simple idea: similarity. In the cell phone service example, how do we predict the outcome for a certain customer? We look at past customers and select the ones who are most similar in features (size of bill, lateness record, yearly income, and so on) to our current customer. If most of those similar customers bolted, we predict the same for the current one. Of course, we are not guaranteed that outcome, but it is our best guess.
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.
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