/machine-learning

Introduction to Machine Learning

Primary LanguageJupyter Notebook

Machine Leaning Class Lectures

Outline

  1. Introduction to Machine Learning
  2. Supervised Learning
  3. Classification, KNN
  4. Decision Trees
  5. Classification Performance Metrics
  6. Bayes classifiers
  7. Support Vector Machine
  8. –
  9. Regression
  10. Dimensionality Reduction/ Feature Selection
  11. Cluster Analysis (K-Means)
  12. Hierarchical Clustering
  13. Association Rule Mining
  14. Ensemble Learning
  15. Wrap-Up (Recap)

Notes

  • Code and supplementary data from lectures will be shared here
  • You can use any IDE of your choice, however jupyter is recommended for this course