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Welcome to Data Mining Course
Foundation from the Python Data Science Handbook
Part 1: Foundation from the Python Data Science Handbook
Machine Learning
Part 2: Machine Learning
What Is Machine Learning?
Introducing Scikit-Learn
Hyperparameters and Model Validation
Feature Engineering
Naive Bayes Classification
Bayesian Decision Theory
Understanding the Covariance Matrix
Maximum Likelihood Estimation
Mahalanobis Distance
k-Nearest Neighbors and Classification Evaluation Metrics
Voronoi Diagrams and Their Connections to Classification
Clustering
k-means Clustering
Gaussian Mixture Models, Part 1
Gaussian Mixture Model, Part 2
Linear Regression, Part 1
Linear Regression, Part 2
Clustering Validation Metrics
Principal Component Analysis, Part 1
Principal Component Analysis, Part 2
Support Vector Machines
Decision Trees and Random Forests
Manifold Learning
Kernel Density Estimation
Application: A Face Detection Pipeline
Appendix: Mathematics and Machine Learning
Repository
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Index
