"Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math.
As you work through the book, you'll learn how to implement a range of powerful ML techniques, starting with the k-Nearest Neighbors (k-NN) method and random forests, and moving on to gradient boosting, support vector machines (SVMs), neural networks, and more.
With the aid of real datasets, you'll delve into regression models through the use of a bike-sharing dataset, explore decision trees by leveraging New York City taxi data, and dissect parametric methods with baseball player stats. You'll also find expert tips for avoiding common problems, like handling ""dirty"" or unbalanced data, and how to troubleshoot pitfalls.
You'll also explore-
How to deal with large datasets and techniques for dimension reduction Details on how the Bias-Variance Trade-off plays out in specific ML methods Models based on linear relationships, including ridge and LASSO regression Real-world image and text classification and how to handle time series data
Machine learning is an art that requires careful tuning and tweaking. With The Art of Machine Learning as your guide, you'll master the underlying principles of ML that will empower you to effectively use these models, rather than simply provide a few stock actions with limited practical use.
Requirements- A basic understanding of graphs and charts and familiarity with the R programming language
Learn to expertly apply a range of machine learning methods to real data with this practical guide.
Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math.
As you work through the book, you'll learn how to implement a range of powerful ML techniques, starting with the k-Nearest Neighbors (k-NN) method and random forests, and moving on to gradient boosting, support vector machines (SVMs), neural networks, and more.
With the aid of real datasets, you'll delve into regression models through the use of a bike-sharing dataset, explore decision trees by leveraging New York City taxi data, and dissect parametric methods with baseball player stats. You'll also find expert tips for avoiding common problems, like handling ""dirty"" or unbalanced data, and how to troubleshoot pitfalls.
You'll also explore-
How to deal with large datasets and techniques for dimension reduction Details on how the Bias-Variance Trade-off plays out in specific ML methods Models based on linear relationships, including ridge and LASSO regression Real-world image and text classification and how to handle time series data
Machine learning is an art that requires careful tuning and tweaking. With The Art of Machine Learning as your guide, you'll master the underlying principles of ML that will empower you to effectively use these models, rather than simply provide a few stock actions with limited practical use.
Requirements- A basic understanding of graphs and charts and familiarity with the R programming language"
By:
Norman Matloff
Imprint: No Starch Press,US
Country of Publication: United States
Dimensions:
Height: 235mm,
Width: 178mm,
Weight: 369g
ISBN: 9781718502109
ISBN 10: 1718502109
Pages: 272
Publication Date: 13 February 2024
Audience:
General/trade
,
ELT Advanced
Format: Paperback
Publisher's Status: Active
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”!
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).
Reviews for The Art Of Machine Learning: A Hands-On Guide to Machine Learning with R
"""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"
