Machine Learning With Python for Everyone

Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use. - Understand machine learning algorithms, models, and core machine learning concepts - Classify examples with classifiers, and quantify examples with regressors - Realistically assess performance of machine learning systems - Use feature engineering to smooth rough data into useful forms - Chain multiple components into one system and tune its performance - Apply machine learning techniques to images and text - Connect the core concepts to neural networks and graphical models - Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.