فهرست مطالب

• Preface
• I. 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
• Main Challenges of Machine Learning
• Testing and Validating
• Exercises
• 2. End-to-End Machine Learning Project
• Working with Real Data
• Look at the Big Picture
• Get the Data
• Explore and Visualize the Data to Gain Insights
• Prepare the Data for Machine Learning Algorithms
• Select and Train a Model
• Fine-Tune Your Model
• Launch, Monitor, and Maintain Your System
• Try It Out!
• Exercises
• 3. Classification
• MNIST
• Training a Binary Classifier
• Performance Measures
• Multiclass Classification
• Error Analysis
• Multilabel Classification
• Multioutput Classification
• Exercises
• 4. Training Models
• Linear Regression
• Gradient Descent
• Polynomial Regression
• Learning Curves
• Regularized Linear Models
• Logistic Regression
• Exercises
• 5. Support Vector Machines
• Linear SVM Classification
• Nonlinear SVM Classification
• SVM Regression
• Under the Hood of Linear SVM Classifiers
• The Dual Problem
• Exercises
• 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
• Sensitivity to Axis Orientation
• Decision Trees Have a High Variance
• Exercises
• 7. Ensemble Learning and Random Forests
• Voting Classifiers
• Bagging and Pasting
• Random Forests
• Boosting
• Stacking
• Exercises
• 8. Dimensionality Reduction
• The Curse of Dimensionality
• Main Approaches for Dimensionality Reduction
• PCA
• Random Projection
• LLE
• Other Dimensionality Reduction Techniques
• Exercises
• 9. Unsupervised Learning Techniques
• Clustering Algorithms: k-means and DBSCAN
• Gaussian Mixtures
• Exercises
• II. Neural Networks and Deep Learning
• 10. Introduction to Artificial Neural Networks with Keras
• From Biological to Artificial Neurons
• Implementing MLPs with Keras
• Fine-Tuning Neural Network Hyperparameters
• Exercises