Feature Engineering For Machine Learning: Principles And Techniques For Data Scientists

Feature Engineering For Machine Learning: Principles And Techniques For Data Scientists

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SKU: SONG1491953241
UPC: 9781491953242
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Feature Engineering Is A Crucial Step In The Machinelearning Pipeline, Yet This Topic Is Rarely Examined On Its Own. With This Practical Book, Youll Learn Techniques For Extracting And Transforming Featuresthe Numeric Representations Of Raw Datainto Formats For Machinelearning 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.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 Realworld, Structured Dataset With Several Featureengineering Techniques. Python Packages Including Numpy, Pandas, Scikitlearn, And Matplotlib Are Used In Code Examples.Youll Examine: Feature Engineering For Numeric Data: Filtering, Binning, Scaling, Log Transforms, And Power Transforms Natural Text Techniques: Bagofwords, Ngrams, And Phrase Detection Frequencybased Filtering And Feature Scaling For Eliminating Uninformative Features Encoding Techniques Of Categorical Variables, Including Feature Hashing And Bincounting Modelbased Feature Engineering With Principal Component Analysis The Concept Of Model Stacking, Using Kmeans As A Featurization Technique Image Feature Extraction With Manual And Deeplearning Techniques

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

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