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Python Machine Learning


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
  • 4. Training Models
    • 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
        AdaBoost Gradient Boosting

    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

        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

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