{"product_id":"the-art-of-machine-learning-isbn-9781718502109","title":"The Art of Machine Learning","description":"\u003cb\u003eLearn to expertly apply a range of machine learning methods to real data with this practical guide.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePacked with real datasets and practical examples, \u003ci\u003eThe Art of Machine Learning\u003c\/i\u003e will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math.\u003cbr\u003e\u003cbr\u003eAs 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.\u003cbr\u003e\u003cbr\u003eWith 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.\u003cbr\u003e\u003cbr\u003eYou’ll also explore:\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eHow to deal with large datasets and techniques for dimension reduction\u003c\/li\u003e\n\u003cli\u003eDetails on how the Bias-Variance Trade-off plays out in specific ML methods\u003c\/li\u003e\n\u003cli\u003eModels based on linear relationships, including ridge and LASSO regression\u003c\/li\u003e\n\u003cli\u003eReal-world image and text classification and how to handle time series data\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eMachine learning is an art that requires careful tuning and tweaking. With\u003ci\u003e The Art of Machine Learning\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003ci\u003eRequirements: A basic understanding of graphs and charts and familiarity with the R programming language\u003c\/i\u003e\u003cb\u003eAcknowledgments\u003cbr\u003eIntroduction\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODS\u003c\/b\u003e\u003cbr\u003eChapter 1: Regression Models\u003cbr\u003eChapter 2: Classification Models\u003cbr\u003eChapter 3: Bias, Variance, Overfitting, and Cross-Validation\u003cbr\u003eChapter 4: Dealing with Large Numbers of Features\u003cbr\u003e\u003cb\u003ePART II: TREE-BASED METHODS\u003c\/b\u003e\u003cbr\u003eChapter 5: A Step Beyond k-NN: Decision Trees\u003cbr\u003eChapter 6: Tweaking the Trees\u003cbr\u003eChapter 7: Finding a Good Set of Hyperparameters\u003cbr\u003e\u003cb\u003ePART III: METHODS BASED ON LINEAR RELATIONSHIPS\u003c\/b\u003e\u003cbr\u003eChapter 8: Parametric Methods\u003cbr\u003eChapter 9: Cutting Things Down to Size: Regularization\u003cbr\u003e\u003cb\u003ePART IV: METHODS BASED ON SEPARATING LINES AND PLANES\u003c\/b\u003e\u003cbr\u003eChapter 10: A Boundary Approach: Support Vector Machines\u003cbr\u003eChapter 11: Linear Models on Steroids: Neural Networks\u003cbr\u003e\u003cb\u003ePART V: APPLICATIONS\u003c\/b\u003e\u003cbr\u003eChapter 12: Image Classification \u003cbr\u003eChapter 13: Handling Time Series and Text Data \u003cbr\u003e\u003cb\u003eAppendix A: List of Acronyms and Symbols \u003cbr\u003eAppendix B: Statistics and ML Terminology Correspondence\u003cbr\u003eAppendix C: Matrices, Data Frames, and Factor Conversions\u003cbr\u003eAppendix D: Pitfall: Beware of “p-Hacking”!\u003c\/b\u003e\"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.\"\u003cbr\u003e\u003cb\u003e—Toby Dylan Hocking, Assistant Professor, Northern Arizona University\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\"\u003ci\u003eThe Art of Machine Learning\u003c\/i\u003e 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.\"\u003cbr\u003e\u003cb\u003e—Dirk Eddelbuettel, Department of Statistics, University of Illinois\u003c\/b\u003e\u003cb\u003eNorman Matloff\u003c\/b\u003e is an award-winning professor at the University of California, Davis. Matloff has a PhD in mathematics from UCLA and is the author of \u003ci\u003eThe Art of Debugging with GDB, DDD, and Eclipse\u003c\/i\u003e and \u003ci\u003eThe Art of R Programming\u003c\/i\u003e (both from No Starch Press).","brand":"No Starch Press","offers":[{"title":"Default Title","offer_id":46305423687909,"sku":"NP9781718502109","price":49.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781718502109.jpg?v=1767738166","url":"https:\/\/k12savings.com\/products\/the-art-of-machine-learning-isbn-9781718502109","provider":"K12savings","version":"1.0","type":"link"}