PrefacePart 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 tepping 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 Check the Assumptions Get the Data Create the Workspace Download the Data Take a Quick Look at the Data Structure Create a Test Set Discover and Visualize the Data to Gain Insights Visualizing Geographical Data Looking for Correlations Experimenting with Attribute Combinations Prepare the Data for Machine Learning Algorithms Data Cleaning Handling Text and Categorical Attributes Custom Transformers Feature Scaling Transformation Pipelines Select and Train a Model Training and Evaluating on the Training Set Better Evaluation Using Cross-Validation Fine-Tune Your Model Grid Search Randomized Search Ensemble Methods Analyze the Best Models and Their Errors Evaluate Your System on the Test Set Launch, Monitor, and Maintain Your System Try It Out! Exercises 3. Classification MNIST Training a Binary Classifier Performance Measures Measuring Accuracy Using Cross-Validation Confusion Matrix Precision and Recall Precision/Recall Tradeoff The ROC Curve Multiclass Classification Error Analysis Multilabel Classification Multioutput Classification���� Part II. Neural Networks and Deep LearningA. Exercise SolutionsB. Machine Learning Project ChecklistC. SVM Dual ProblemD. AutodiffE. Other Popular ANN ArchitecturesIndex