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Course Information
Course Learning Objectives
Notes
Course Introduction
Lecture 1: Terminology, Baselines, Decision Trees
Lecture 2: Machine Learning Fundamentals
Lecture 3:
\(k\)
-Nearest Neighbours and SVM RBFs
Lecture 4: Preprocessing,
sklearn
Pipeline,
sklearn
ColumnTrasnsformer
Lecture 5: More preprocessing, text features
Lecture 6: Hyperparameter Optimization and Optimization Bias
Lecture 7: Naive Bayes
Lecture 8: Linear Models
Appendix A: Multi-class, meta-strategies
Class Demos
Lecture 2: Class demo
Lecture 3: Class demo
Lecture 5: Class demo
Lectures 5: Class demo
Lectures 6: Class demo
Lecture 7: Class demo
Lectures 8: Class demo
Section slides
Section 101 Classifiers
Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 5
Lecture 6
Lecture 7
Lecture 8
Section 102 Regressors
Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 5
Lecture 6
Lecture 7
Lecture 8
Attribution
Attributions
LICENSE
Index
