Hands-On Machine Learning with Scikit-Learn& TensorFlow  P500+(英文，可编辑的pdf电子文档）

Textbook:
Hands-On Machine Learning with Scikit-Learn& TensorFlow
CONCEPTS, TOOLS, AND TECHNIQUES TO BUILD INTELLIGENT SYSTEMS
Aurélien Géron

contents:
Part I. The Fundamentals of Machine Learning
1. The Machine Learning Landscape.
What Is Machine Learning?
Why Use Machine Learning?
Types of Machine Learning Systems Supervised/Unsupervised Learning Batch and Online Learning Instance-Based Versus Model-Based Learning Main Challenges of Machine Learning Insufficient Quantity of Training Data Nonrepresentative Training Data Poor-Quality Data Irrelevant Features Overfitting the Training Data Underfitting the Training Data Stepping Back Testing and Validating Exercises
2. End-to-End Machine Learning Project.
Working with Real Data Look at the Big Picture Frame the Problem Select a Performance Measure

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