Feature Engineering for Machine Learning (Paperback)

Alice Zheng

ISBN 10: 1491953241 / ISBN 13: 9781491953242

Edité par O'Reilly Media, Sebastopol, 2018

A propos de cet article

Paperback. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youundefinedll learn techniques for extracting and transforming featuresundefinedthe numeric representations of raw dataundefinedinto 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.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.Youundefinedll examine:Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transformsNatural text techniques: bag-of-words, n-grams, and phrase detectionFrequency-based filtering and feature scaling for eliminating uninformative featuresEncoding techniques of categorical variables, including feature hashing and bin-countingModel-based feature engineering with principal component analysisThe concept of model stacking, using k-means as a featurization techniqueImage feature extraction with manual and deep-learning techniquesAbout the AuthorAlice 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. Principal Product Manager + Data Scientist for Concur Labs at SAP Concur, designing prototypes, interfaces and future tech for travel and expense. Amanda experiments with projects and programs to make machine learning more accessible. Her side projects include volunteering with the NASA Datanauts and getting outside as much as possible. 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 featuresthe numeric representations of raw datainto formats for machine-learning models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

N° de réf. du vendeur 9781491953242

Détails bibliographiques

• Titre: Feature Engineering for Machine Learning (Paperback)
• Éditeur: O'Reilly Media, Sebastopol
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 1491953241
• ISBN à 13 chiffres: 9781491953242
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

Synopsis

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.

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.

You'll examine:

• Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms
• Natural text techniques: bag-of-words, n-grams, and phrase detection
• Frequency-based filtering and feature scaling for eliminating uninformative features
• Encoding techniques of categorical variables, including feature hashing and bin-counting
• Model-based feature engineering with principal component analysis
• The concept of model stacking, using k-means as a featurization technique
• Image feature extraction with manual and deep-learning techniques

À propos de l?auteur

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.