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Course Information
Course Learning Objectives
Course Introduction
Lectures
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
Class demos
Lecture 2: Class demo
Lecture 3: Class demo
Lecture 4: Class demo
Lectures 5: Class demo
Lectures 8: Class demo
Attribution
Attributions
LICENSE
Index
