The Art Of Machine Learning

a hand-on guide to machine learning with R

Norman Matloff (Autor)

Buch | Softcover

272 Seiten

2024 | 1. Auflage
No Starch Press,US (Verlag)
978-1-7185-0210-9 (ISBN)

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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

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).

Acknowledgments
Introduction
PART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODS
Chapter 1: Regression Models
Chapter 2: Classification Models
Chapter 3: Bias, Variance, Overfitting, and Cross-Validation
Chapter 4: Dealing with Large Numbers of Features
PART II: TREE-BASED METHODS
Chapter 5: A Step Beyond k-NN: Decision Trees
Chapter 6: Tweaking the Trees
Chapter 7: Finding a Good Set of Hyperparameters
PART III: METHODS BASED ON LINEAR RELATIONSHIPS
Chapter 8: Parametric Methods
Chapter 9: Cutting Things Down to Size: Regularization
PART IV: METHODS BASED ON SEPARATING LINES AND PLANES
Chapter 10: A Boundary Approach: Support Vector Machines
Chapter 11: Linear Models on Steroids: Neural Networks
PART V: APPLICATIONS
Chapter 12: Image Classification
Chapter 13: Handling Time Series and Text Data
Appendix A: List of Acronyms and Symbols
Appendix B: Statistics and ML Terminology Correspondence
Appendix C: Matrices, Data Frames, and Factor Conversions
Appendix D: Pitfall: Beware of “p-Hacking”!

Erscheinungsdatum	24.12.2023
Zusatzinfo	Illustrationen
Verlagsort	San Francisco
Sprache	englisch
Maße	178 x 235 mm
Einbandart	kartoniert
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
ISBN-10	1-7185-0210-9 / 1718502109
ISBN-13	978-1-7185-0210-9 / 9781718502109
Zustand	Neuware