Pay in installments of $95.62 with
,
and
Shipping Estimate
USA
- USA
- CAN
- USA
- CAN
Ships within 48 hours · Estimated delivery Jul 20 - Jul 25
Promo Codes Available:
For Your Every Summer RSVP, with Code: SUMMER15
Description
Feature Engineering for Machine LearningFeature 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 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.'box-sizing: border-box; padding: 0px; margin-top: 0em; margin-bottom: 1em; margin-left: 1em; font-family: "Amazon Ember", Arial, sans-serif; font-size: small; background-color: rgb(255, 255, 255);'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 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.'box-sizing: border-box; padding: 0px; margin-top: 0em; margin-bottom: 1em; margin-left: 1em; font-family: "Amazon Ember", Arial, sans-serif; font-size: small; background-color: rgb(255, 255, 255);'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
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy