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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

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By Varada Kolhatkar

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