Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists by Alice Zheng, Amanda Casari
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists Alice Zheng, Amanda Casari ebook
Publisher: O'Reilly Media, Incorporated
Principles and Techniques for DataScientists. Of Winder Research, for an intensive 3-day Data science and Analytics course, that will leave you with practical tools for utilizing Machine Learning principles in your organisation. Transfer learning: leveraging insights from large data sets. Häftad Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. Using domain knowledge to strengthen your predictive model or prescriptive model out of prediction can be both difficult and expensive. These seven principles work together to drive the Agile data science methodology. Applying methods from Agile software development to data science projects. Feature engineering as an essential to applied machine learning. Bevaka Feature Engineering for Machine Learning Models så får du ett mejl när boken går att köpa. In this blog post, you'll learn what transfer learning is, what some of its applications are and why it is critical skill as a data scientist. Building accurate predictive models can take many iterations of featureengineering and hyperparameter tuning. In data science, iteration is . In this one-day introductory training, you will gain practical experience in the latest Analytics and Data Science technology and techniques.