Volume I – Machine Learning 1. The Machine Learning Landscape 1. What is Machine Learning? 2. Why use Machine Learning? 3. Examples of Applications: types of machine learning systems, supervised and unsupervised learning, batch and online learning instance-based versus model-based learning. 4. Main Challenges of Machine Learning: insufficient quantity of training data, nonrepresentative training data, poor-quality data, irrelevant features, overfitting the training data, stepping back. 5. Testing and Validating: hyperparameter tuning and model selection 6. Data Mismatch 2. End-to-End Machine Learning Project 1. Working with Real-World Data: 2. Looking at the Bigger Picture: framing the problem, selecting a performance metric, checking the assumptions. 3. Getting the Data: creating the workspace, downloading the data, understanding the data structure, creating a test set 4. Discovering and Visualizing the Data to Gain Insight: visualizing geographical data, looking for correlations, experimenting with attribute combinations. 5. Preparing the Data for Machine Learning Algorithms: data cleaning, handling text and categorical attributes, custom transformers, feature scaling, transformation pipelines. 6. Selecting and Training a Model: training and evaluating on the training set, better evaluation using cross-validation. 7. Fine-Tuning the Model: grid search, randomized search, ensemble methods, analyze the best models and their errors, evaluate the model on the test set 8. Launching, Monitoring, and Maintaining the Model 3. Classification 1. MNIST 2. Training a Binary Classifier 3. Performance Metrics: measuring accuracy using cross-validation, confusion matrix, precision and recall, precision-recall trade-off, the ROC curve. 4. Multiclass Classification 5. Error Analysis 6. Multilabel Classification 7. Multioutput Classification 4. Training Models 1. Linear Regression: the normal equation, computational complexity 2. Gradient Descent: batch gradient descent, stochastic gradient descent, mini-batch gradient descent 3. Polynomial Regression 4. Learning Curves 5. Regularized Linear Models: ridge regression, LASSO regression, elastic net, early stopping. 6. Logistic Regression: estimating probabilities, training and cost functions, decision boundaries, Softmax regression. 5. Support Vector Machines1. Linear SVM Classification: soft margin classification 2. Nonlinear SVM Classification: polynomial kernel, similarity features, Gaussian RBF kernel, computational complexity. 3. SVM Regression 4. Under the Hood: decision function and predictions, training objectives, quadratic programming, the dual problem, kernelized SVMs, online SVMs. 6. Decision Trees 1. Training and Visualizing a Decision Tree: 2. Making Predictions 3. Estimating Class Probabilities 4. The CART Training Algorithm 5. Computational Complexity 6. Gini Impurity or Entropy 7. Regularization Hyperparameters 8. Regression 9. Instability 7. Ensemble Learning and Random Forests 1. Voting Classifiers: 2. Bagging and Pasting: Scikit-Learn in bagging and pasting 3. Random Patches and Random Subspaces: 4. Random Forests: extra-trees, feature importance 5. Boosting: Adaboost, gradient boosting 6. Stacking 8. Dimensionality Reduction 1. The Curse of Dimensionality 2. Main Approaches for Dimensionality Reduction: projection, manifold learning. 3. Principal Component Analysis: preserving the variance, principal components, projecting down to a minimum number of dimensions, Use of Scikit-Learn, explained variance ratio, choosing the right number of dimensions, PCA for compression, randomized PCA, incremental PCA. 4. Kernel PCA: selecting a Kernel and tuning hyperparameters 5. Locally Linear Embedding 6. Other Dimensionality Reduction Techniques 9. Unsupervised Learning Techniques 1. Clustering: K-means, limits of k-means, using clustering for image segmentation, using clustering for pre-processing, using clustering for semi-supervised learning, DBSCAN, other clustering algorithms. 2. Gaussian Mixtures: Anomaly detection using Gaussian mixtures, selecting the number of clusters, Bayesian Gaussian Mixture Models, Other algorithms for anomaly and novelty detection