The Art of Machine Learning: A Hands-On Guide to Machine Learning with R

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

學習如何將各種機器學習方法應用於真實數據的實用指南。本書充滿了真實數據集和實際例子，將幫助您直觀地理解機器學習方法的工作原理和原因，而無需深入的數學知識。在閱讀本書的過程中，您將學習如何實施一系列強大的機器學習技術，從最基礎的k-最近鄰(k-NN)方法和隨機森林開始，進而學習梯度提升、支持向量機(SVM)、神經網絡等更高級的技術。通過使用真實數據集，您將通過使用自行車共享數據集來深入研究回歸模型，通過使用紐約市出租車數據來探索決策樹，並通過棒球選手數據來解剖參數方法。您還將找到專家提示，以避免常見問題，例如處理“骯髒”或不平衡的數據，以及如何解決問題。您還將探索以下內容：如何處理大型數據集以及降維技術的方法，偏差-方差平衡在特定機器學習方法中的作用，基於線性關係的模型，包括岭回歸和LASSO回歸，以及現實世界中的圖像和文本分類以及如何處理時間序列數據。機器學習是一門需要仔細調整和調整的藝術。有了《機器學習的藝術》作為指南，您將掌握機器學習的基本原理，使您能夠有效地使用這些模型，而不僅僅是提供一些有限實用性的標準操作。要求：對圖表和圖表有基本的理解，並熟悉R編程語言。

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).

Norman Matloff是加州大學戴維斯分校的屢獲殊榮的教授。Matloff在加州大學洛杉磯分校獲得數學博士學位，並且是《The Art of Debugging with GDB, DDD, and Eclipse》和《The Art of R Programming》（兩者皆由No Starch Press出版）的作者。