Feature Engineering for Machine Learning - by Alice Zheng & Amanda Casari (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>"Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming features-the numeric representations of raw data-into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering."--Page 4 of cover.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features--the numeric representations of raw data--into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.</p><p>Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.</p><p>You'll examine: </p><ul><li>Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms</li><li>Natural text techniques: bag-of-words, n-grams, and phrase detection</li><li>Frequency-based filtering and feature scaling for eliminating uninformative features</li><li>Encoding techniques of categorical variables, including feature hashing and bin-counting</li><li>Model-based feature engineering with principal component analysis</li><li>The concept of model stacking, using k-means as a featurization technique</li><li>Image feature extraction with manual and deep-learning techniques</li></ul><p/><br></br><p><b> About the Author </b></p></br></br><p>Alice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.</p>
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