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Preface
Part ¢ñ£®The Fundamentals of Machine Learning
1£®TheMachine Learning Landscape
What Is Machine Learning
Whr Use Machine Learning
Examples of Applications
Types of Machine Learning Systems
Training Supervision
Batch Versus 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
Hyperparameter Tuning and Model Selection
Data Mismatch
Exercises
2£®End-to-End Machine Learning Project
Working with Real Data
Look at the Big Picture
Frame the Problem
Select a Performance Measure
Check the Assumptions
Get the Data
Running the Code Examples Using Google Colab
Saving Your Code Changes and Your Data
The Power and Danger of Interactivity
Book Code Versus Notebook Code
Download the Data
Take a Quick Look at the Data Structure
Create a 11est Set
Explore and Visualize the Data to Gain Insights
Visualizing Geographical Data
Look for Correlations
Experiment with Attribute Combinations
Prepare the Data for Machine Learning Algorithms
Clean the Data
Handling Text and Categorical Attributes
Feature Scaling and Transformation
Custom Transformers
Transformation Pipelines
Select and Train a Model
Train and Evaluate on the Training Set
Better Evaluation Using Cross-Validation
Fine-Tune Your Model
Grid Search
Randomized Search
Ensemble Methods
Analyzing the Best Models and Their Errors
Evaluate Your System on the Test Set
Launch£¬Monitor,and Maintain Your System
TryItout
Exercises
3£®Classification
MNIST
Training a Binary Classifier
Performance Measures
Measuring Accuracy Using Cross-Validation
Confusion Matrices
Precision and Recall
The Precision£¯Recall Trade-off
The ROC Curve
Multiclass Classification
Error Analysis
Multilabel Classification
Multioutput Classification
Exercises
¡­¡­
Part ¢ò Neural Networks and Deep Learning
A£®Machine Learning Project Checklist
B£®Autodiff
C£®SpecialData Structures
D£®TensorFIowGraphs
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