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 Neighbours 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, su
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AcknowledgmentsIntroductionPART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODSChapter 1: Regression ModelsChapter 2: Classification ModelsChapter 3: Bias, Variance, Overfitting, and Cross-ValidationChapter 4: Dealing with Large Numbers of FeaturesPART II: TREE-BASED METHODSChapter 5: A Step Beyond k-NN: Decision TreesChapter 6: Tweaking the TreesChapter 7: Finding a Good Set of HyperparametersPART III: METHODS BASED ON LINEAR RELATIONSHIPSChapter 8: Parametric MethodsChapter 9: Cutting Things Down to Size: RegularizationPART IV: METHODS BASED ON SEPARATING LINES AND PLANESChapter 10: A Boundary Approach: Support Vector MachinesChapter 11: Linear Models on Steroids: Neural NetworksPART V: APPLICATIONSChapter 12: Image Classification Chapter 13: Handling Time Series and Text Data Appendix A: List of Acronyms and Symbols Appendix B: Statistics and ML Terminology CorrespondenceAppendix C: Matrices, Data Frames, and Factor ConversionsAppendix D: Pitfall: Beware of “p-Hacking”!
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"In contrast to other books about machine learning, there is a bigger emphasis on programming and usage in practice. In particular, there is an excellent explanation of how to avoid over/under-fitting, and how to use cross-validation. This book is sure to be helpful for students who are interested to understand the core concepts, as well as their practical implementations in R."—Toby Dylan Hocking, Assistant Professor, Northern Arizona University"The Art of Machine Learning by Norman Matloff is a welcome addition to a growing body of books about machine learning. Matloff, whose career spans both computer science and statistics, addresses the new and exciting field with a fresh approach."—Dirk Eddelbuettel, Department of Statistics, University of Illinois
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Produktdetaljer

ISBN
9781718502109
Publisert
2024-01-09
Utgiver
Vendor
No Starch Press,US
Høyde
235 mm
Bredde
178 mm
Aldersnivå
G, 01
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
272

Forfatter

Om bidragsyterne

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).