Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

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
**
### Sinossi
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
### L’autore
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.

Only registered users can download this free product.
Genere: SKU: 41469 Tags:

Recensioni

Ancora non ci sono recensioni.

Recensisci per primo “Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists”