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Table of Contents
Intro; Preface; Reading the Book; Acknowledgments; Contents; List of Figures; List of Tables; Chapter 1: Machine Learning Definition and Basics; 1.1 Introduction; 1.1.1 Resurgence of ML; 1.1.2 Relation with Artificial Intelligence (AI); 1.1.3 Machine Learning Problems; 1.2 Matrices; 1.2.1 Vector and Tensors; 1.2.2 Matrix Addition (or Subtraction); 1.2.3 Matrix Transpose; 1.2.4 Matrix Multiplication; 1.2.4.1 Multiplying with a Scalar; 1.2.4.2 Multiplying with Another Matrix; 1.2.4.3 Multiplying with a Vector; 1.2.5 Identity Matrix; 1.2.6 Matrix Inversion; 1.2.7 Solving Equations Using Matrices
1.3 Numerical Methods1.4 Probability and Statistics; 1.4.1 Sampling the Distribution; 1.4.2 Random Variables; 1.4.3 Expectation; 1.4.4 Conditional Probability and Distribution; 1.4.5 Maximum Likelihood; 1.5 Linear Algebra; 1.6 Differential Calculus; 1.6.1 Functions; 1.6.2 Slope; 1.7 Computer Architecture; 1.8 Next Steps; Chapter 2: Learning Models; 2.1 Supervised Learning; 2.1.1 Classification Problem; 2.1.2 Regression Problem; 2.2 Unsupervised Learning; 2.3 Semi-supervised Learning; 2.4 Reinforcement Learning; Chapter 3: Regressions; 3.1 Introduction; 3.2 The Model; 3.3 Problem Formulation
3.4 Linear Regression3.4.1 Normal Method; 3.4.2 Gradient Descent Method; 3.4.2.1 Determine the Slope at Any Given Point; 3.4.2.2 Initial Value; 3.4.2.3 Correction; 3.4.2.4 Learning Rate; 3.4.2.5 Convergence; 3.4.2.6 Alternate Method for Computing Slope; 3.4.2.7 Putting Gradient Descent in Practice; 3.4.3 Normal Equation Method vs Gradient Descent Method; 3.5 Logistic Regression; 3.5.1 Sigmoid Function; 3.5.2 Cost Function; 3.5.3 Gradient Descent; 3.6 Next Steps; 3.7 Key Takeaways; Chapter 4: Improving Further; 4.1 Nonlinear Contribution; 4.2 Feature Scaling
4.5.2.1 Basic Approach for SoftMax4.5.2.2 Loss Function; 4.6 Key Takeaways and Next Steps; Chapter 5: Classification; 5.1 Decision Boundary; 5.1.1 Nonlinear Decision Boundary; 5.2 Skewed Class; 5.2.1 Optimizing Precision vs Recall; 5.2.2 Single Metric; 5.3 Naïve Bayes ́Algorithm; 5.4 Support Vector Machines; 5.4.1 Kernel Selection; Chapter 6: Clustering; 6.1 K-Means; 6.1.1 Basic Algorithm; 6.1.2 Distance Calculation; 6.1.3 Algorithm Pseudo Code; 6.1.4 Cost Function; 6.1.5 Choice of Initial Random Centers; 6.1.6 Number of Clusters; 6.2 K-Nearest Neighbor (KNN); 6.2.1 Weight Consideration
1.3 Numerical Methods1.4 Probability and Statistics; 1.4.1 Sampling the Distribution; 1.4.2 Random Variables; 1.4.3 Expectation; 1.4.4 Conditional Probability and Distribution; 1.4.5 Maximum Likelihood; 1.5 Linear Algebra; 1.6 Differential Calculus; 1.6.1 Functions; 1.6.2 Slope; 1.7 Computer Architecture; 1.8 Next Steps; Chapter 2: Learning Models; 2.1 Supervised Learning; 2.1.1 Classification Problem; 2.1.2 Regression Problem; 2.2 Unsupervised Learning; 2.3 Semi-supervised Learning; 2.4 Reinforcement Learning; Chapter 3: Regressions; 3.1 Introduction; 3.2 The Model; 3.3 Problem Formulation
3.4 Linear Regression3.4.1 Normal Method; 3.4.2 Gradient Descent Method; 3.4.2.1 Determine the Slope at Any Given Point; 3.4.2.2 Initial Value; 3.4.2.3 Correction; 3.4.2.4 Learning Rate; 3.4.2.5 Convergence; 3.4.2.6 Alternate Method for Computing Slope; 3.4.2.7 Putting Gradient Descent in Practice; 3.4.3 Normal Equation Method vs Gradient Descent Method; 3.5 Logistic Regression; 3.5.1 Sigmoid Function; 3.5.2 Cost Function; 3.5.3 Gradient Descent; 3.6 Next Steps; 3.7 Key Takeaways; Chapter 4: Improving Further; 4.1 Nonlinear Contribution; 4.2 Feature Scaling
4.5.2.1 Basic Approach for SoftMax4.5.2.2 Loss Function; 4.6 Key Takeaways and Next Steps; Chapter 5: Classification; 5.1 Decision Boundary; 5.1.1 Nonlinear Decision Boundary; 5.2 Skewed Class; 5.2.1 Optimizing Precision vs Recall; 5.2.2 Single Metric; 5.3 Naïve Bayes ́Algorithm; 5.4 Support Vector Machines; 5.4.1 Kernel Selection; Chapter 6: Clustering; 6.1 K-Means; 6.1.1 Basic Algorithm; 6.1.2 Distance Calculation; 6.1.3 Algorithm Pseudo Code; 6.1.4 Cost Function; 6.1.5 Choice of Initial Random Centers; 6.1.6 Number of Clusters; 6.2 K-Nearest Neighbor (KNN); 6.2.1 Weight Consideration