The Fundamentals of Machine Learning

1.The Machine Learning Landscape

What Is Machine Learning? Why Use Machine Learning? Examples of Applications 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
Hyperparameter Tuning and Model Selection Data Mismatch

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!

3. Classification

MNIST Training a Binary Classifier Performance Measures
Measuring Accuracy Using Cross-Validation Confusion Matrix Precision and Recall Precision/Recall Trade-off The ROC Curve
Multiclass Classification Error Analysis Multilabel Classification Multioutput Classification

Linear Regression
The Normal Equation Computational Complexity
Gradient Descent
Batch Gradient Descent Stochastic Gradient Descent Mini-batch Gradient Descent
Polynomial Regression Learning Curves Regularized Linear Models
Lasso Regression Elastic Net Early Stopping
• Logistic Regression
  Estimating Probabilities Training and Cost Function Decision Boundaries Softmax Regression

5. Support Vector Machines

Linear SVM Classification Soft Margin Classification
Nonlinear SVM Classification
Polynomial Kernel Similarity Features Gaussian RBF Kernel Computational Complexity
SVM Regression Under the Hood
Decision Function and Predictions Training Objective Quadratic Programming The Dual Problem Kernelized SVMs Online SVMs

6. Decision Trees

Training and Visualizing a Decision Tree Making Predictions Estimating Class Probabilities The CART Training Algorithm Computational Complexity Gini Impurity or Entropy? Regularization Hyperparameters Regression Instability Exercises

7. Ensemble Learning and Random Forests

Voting Classifiers Bagging and Pasting
Bagging and Pasting in Scikit-Learn Out-of-Bag Evaluation
Random Patches and Random Subspaces Random Forests
Extra-Trees Feature Importance
Boosting
AdaBoost Gradient Boosting
Stacking

8. Dimensionality Reduction

The Curse of Dimensionality Main Approaches for Dimensionality Reduction
Projection Manifold Learning
PCA Preserving the Variance Principal Components Projecting Down to d Dimensions Using Scikit-Learn Explained Variance Ratio Choosing the Right Number of Dimensions PCA for Compression Randomized PCA Incremental PCA
Kernel PCA
Selecting a Kernel and Tuning Hyperparameters
LLE >Other Dimensionality Reduction Techniques

9. Unsupervised Learning Techniques

Clustering
K-Means Limits of K-Means Using Clustering for Image Segmentation Using Clustering for Preprocessing Using Clustering for Semi-Supervised Learning DBSCAN Other Clustering Algorithms
Gaussian Mixtures
Anomaly Detection Using Gaussian Mixtures Selecting the Number of Clusters Bayesian Gaussian Mixture Models Other Algorithms for Anomaly and Novelty Detection