Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. Alice Zheng, Amanda Casari

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


Feature-Engineering-for-Machine.pdf
ISBN: 9781491953242 | 214 pages | 6 Mb

Download PDF




  • Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
  • Alice Zheng, Amanda Casari
  • Page: 214
  • Format: pdf, ePub, fb2, mobi
  • ISBN: 9781491953242
  • Publisher: O'Reilly Media, Incorporated
Download Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists


Download a book on ipad Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists in English 9781491953242 by Alice Zheng, Amanda Casari

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists by Alice Zheng, Amanda Casari Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic. Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. If you understand basic machine learning concepts like supervised and unsupervised learning, you’re ready to get started. Not only will you learn how to implement feature engineering in a systematic and principled way, you’ll also learn how to practice better data science. Learn exactly what feature engineering is, why it’s important, and how to do it well Use common methods for different data types, including images, text, and logs Understand how different techniques such as feature scaling and principal component analysis work Understand how unsupervised feature learning works in the case of deep learning for images

Feature Engineering for Machine Learning and Data Analytics
Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation,feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications,  Feature Engineering | freeCodeCamp Guide
Feature Engineering. Machine Learning works best with well formed data.Feature engineering describes certain techniques to make sure we're working with the best possible representation of the data we collected. Following are twotechniques of feature engineering: scaling and selection. Feature Engineering for Machine Learning | Udemy
Beginner Data Scientists who want to get started in pre-processing datasets to build machine learning models; Intermediate Data Scientists who want to level up their experience in feature engineering for machine learning; Advanced DataScientists who want to discover new and innovative techniques for feature  Feature Engineering for Machine Learning: Amazon.es: Alice Zheng
To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic.Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. Machine Learning - Data Science and Analytics for Developers
GOTO Academy are excited to bring you UK-based Phil Winder of Winder Research, for an intensive 2-day Data science and Analytics course, that will leave you wit. Holdout and validation techniques; Optimisation and simple data processing; Linear regression; Classification and clustering; Feature engineering  



More eBooks:
Descarga gratuita del libro de la selva TODOS LOS NAUFRAGIOS 9788423355761 (Spanish Edition)