{"product_id":"feature-engineering-for-machine-learning-principles-and-techniques-for-data-scientists-9781491953242","title":"Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists","description":"\u003cp\u003eFeature 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. \u003c\/p\u003e\u003cp\u003e 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. \u003c\/p\u003e\u003cp\u003e You'll examine: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eFeature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms \u003c\/li\u003e\n\u003cli\u003eNatural text techniques: bag-of-words, n-grams, and phrase detection \u003c\/li\u003e\n\u003cli\u003eFrequency-based filtering and feature scaling for eliminating uninformative features \u003c\/li\u003e\n\u003cli\u003eEncoding techniques of categorical variables, including feature hashing and bin-counting \u003c\/li\u003e\n\u003cli\u003eModel-based feature engineering with principal component analysis \u003c\/li\u003e\n\u003cli\u003eThe concept of model stacking, using k-means as a featurization technique \u003c\/li\u003e\n\u003cli\u003eImage feature extraction with manual and deep-learning techniques \u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAlice 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.\u003c\/p\u003e\u003cbr\u003e","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":50466646524178,"sku":"9781491953242","price":64.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_7dc3f935-1a0d-4029-b1e4-4d265b0d8630.jpg?v=1730124852","url":"https:\/\/surprise-castle.myshopify.com\/products\/feature-engineering-for-machine-learning-principles-and-techniques-for-data-scientists-9781491953242","provider":"Surprise Castle","version":"1.0","type":"link"}