The Complete Beginner's Guide to Understanding and Building Machine
Learning Systems with Python
Machine Learning with Python for Everyone will help you master the
processes, patterns, and strategies you need to build effective learning
systems, even if you're an absolute beginner. If you can write some
Python code, this book is for you, no matter how little college-level
math you know. 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
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