Machine Learning Made Visual with Python makes machine learning intuitive through Python coding and dynamic visualizations. The book helps readers grasp complex math concepts by showing how algorithms evolve step-by-step. Readers will learn how to develop a hands-on, visual, and practical path to mastering core machine learning algorithms. Importantly, the book includes practical examples and coding exercises.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Preface - p. xv 8. Acknowledgment - p. xxi 9. Chapter 1. Machine Learning: From Concepts to Practice - p. 1 10. Chapter 2. Mathematical Backbone: Vectors as Building Blocks of Machine Learning - p. 13 11. Chapter 3. The Magical World of Matrices: Building Blocks for Machine Learning - p. 33 12. Chapter 4. Simple Linear Regression: From a Line to a Model - p. 69 13. Chapter 5. Multivariate Linear Regression - p. 85 14. Chapter 6. Polynomial Regression: Making Linear Models Go Nonlinear - p. 97 15. Chapter 7. Probability and Statistics: Foundations for Machine Learning - p. 111 16. Chapter 8. Logistic Regression: From Sigmoid Curves to Classification Boundaries - p. 127 17. Chapter 9. Regularized Regression – Taming Complexity in Machine Learning - p. 143 18. Chapter 10. Eigenvalue Decomposition: Seeing Transformations From Within - p. 159 19. Chapter 11. Covariance Matrix: Understanding Feature Relationships - p. 175 20. Chapter 12. Gaussian Distributions: The Bell Curve in Machine Learning - p. 189 21. Chapter 13. Bayesian Linear Regression – Learning From Both Data and Belief - p. 215 22. Chapter 14. Gaussian Processes: Modeling Uncertainty in Functions - p. 229 23. Chapter 15. k-Nearest Neighbor Classification: Learning by Proximity - p. 241 24. Chapter 16. Distance Measures in Data – Understanding Similarity and Difference - p. 255 25. Chapter 17. Naive Bayes Classification: Predicting With Probabilities - p. 269 26. Chapter 18. Gaussian Discriminant Analysis: From Circles to Ellipses - p. 287 27. Chapter 19. Support Vector Machine: Finding the Perfect Margin - p. 313 28. Chapter 20. The Kernel Trick: Turning Lines Into Curves - p. 331 29. Chapter 21. Decision Trees: From Simple Splits to Smart Predictions - p. 355 30. Chapter 22. Principal Component Analysis: Explained Through Ellipse - p. 375 31. Chapter 23. Principal Component Analysis in Action: From Theory to Real Data - p. 389 32. Chapter 24. Singular Value Decomposition: Understanding the Geometry of Matrices - p. 409 33. Chapter 25. Truncated Singular Value Decomposition: From Geometry to Image Compression - p. 427 34. Chapter 26. Kernel Principal Component Analysis: Unlocking Nonlinear Dimensions - p. 441 35. Chapter 27. Linear Discriminant Analysis: Pushing Classes Apart - p. 453 36. Chapter 28. K-Means Clustering: Chasing the Centroids - p. 461 37. Chapter 29. Gaussian Mixture Models — When One Bell Curve Isn't Enough - p. 471 38. Chapter 30. Mean Shift: Climbing Toward Peaks of Density - p. 485 39. Chapter 31. Hierarchical Clustering: Building a Tree From the Data - p. 495 40. Chapter 32. Density-Based Clustering: From Density Peaks to Natural Clusters - p. 509 41. Chapter 33. Spectral Clustering: Finding Hidden Communities in Networks - p. 515 42. Index - p. 531
Machine Learning Made Visual with Python makes machine learning intuitive through Python coding and dynamic visualizations. The book helps readers grasp complex math concepts by showing how algorithms evolve step-by-step. Readers will learn how to develop a hands-on, visual, and practical path to mastering core machine learning algorithms. Importantly, the book includes practical examples and coding exercises.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Preface - p. xv 8. Acknowledgment - p. xxi 9. Chapter 1. Machine Learning: From Concepts to Practice - p. 1 10. Chapter 2. Mathematical Backbone: Vectors as Building Blocks of Machine Learning - p. 13 11. Chapter 3. The Magical World of Matrices: Building Blocks for Machine Learning - p. 33 12. Chapter 4. Simple Linear Regression: From a Line to a Model - p. 69 13. Chapter 5. Multivariate Linear Regression - p. 85 14. Chapter 6. Polynomial Regression: Making Linear Models Go Nonlinear - p. 97 15. Chapter 7. Probability and Statistics: Foundations for Machine Learning - p. 111 16. Chapter 8. Logistic Regression: From Sigmoid Curves to Classification Boundaries - p. 127 17. Chapter 9. Regularized Regression – Taming Complexity in Machine Learning - p. 143 18. Chapter 10. Eigenvalue Decomposition: Seeing Transformations From Within - p. 159 19. Chapter 11. Covariance Matrix: Understanding Feature Relationships - p. 175 20. Chapter 12. Gaussian Distributions: The Bell Curve in Machine Learning - p. 189 21. Chapter 13. Bayesian Linear Regression – Learning From Both Data and Belief - p. 215 22. Chapter 14. Gaussian Processes: Modeling Uncertainty in Functions - p. 229 23. Chapter 15. k-Nearest Neighbor Classification: Learning by Proximity - p. 241 24. Chapter 16. Distance Measures in Data – Understanding Similarity and Difference - p. 255 25. Chapter 17. Naive Bayes Classification: Predicting With Probabilities - p. 269 26. Chapter 18. Gaussian Discriminant Analysis: From Circles to Ellipses - p. 287 27. Chapter 19. Support Vector Machine: Finding the Perfect Margin - p. 313 28. Chapter 20. The Kernel Trick: Turning Lines Into Curves - p. 331 29. Chapter 21. Decision Trees: From Simple Splits to Smart Predictions - p. 355 30. Chapter 22. Principal Component Analysis: Explained Through Ellipse - p. 375 31. Chapter 23. Principal Component Analysis in Action: From Theory to Real Data - p. 389 32. Chapter 24. Singular Value Decomposition: Understanding the Geometry of Matrices - p. 409 33. Chapter 25. Truncated Singular Value Decomposition: From Geometry to Image Compression - p. 427 34. Chapter 26. Kernel Principal Component Analysis: Unlocking Nonlinear Dimensions - p. 441 35. Chapter 27. Linear Discriminant Analysis: Pushing Classes Apart - p. 453 36. Chapter 28. K-Means Clustering: Chasing the Centroids - p. 461 37. Chapter 29. Gaussian Mixture Models — When One Bell Curve Isn't Enough - p. 471 38. Chapter 30. Mean Shift: Climbing Toward Peaks of Density - p. 485 39. Chapter 31. Hierarchical Clustering: Building a Tree From the Data - p. 495 40. Chapter 32. Density-Based Clustering: From Density Peaks to Natural Clusters - p. 509 41. Chapter 33. Spectral Clustering: Finding Hidden Communities in Networks - p. 515 42. Index - p. 531
