Course curriculum

  • 1

    Decision Trees

    • Introduction to Tree Based Methods

    • Decision Tree Theory - History

    • Decision Tree Theory - Terminology

    • Decision Tree Theory - Gini Impurity

    • Decision Tree Theory - Gini Impurity in Trees Part One

    • Decision Tree Theory - Gini Impurity in Trees Part Two

    • Decision Trees with Scikit-Learn Part One

    • Decision Trees with Scikit-Learn Part Two

  • 2

    Random Forests

    • Introduction to Random Forests

    • Random Forest Theory - History and Motivation

    • Random Forest Theory - Hyperparameters Overview

    • Random Forest Theory - Hyperparameters - Number of Estimators and Features

    • Random Forest Theory - Hyperparameters - Bootstrapping

    • Random Forest - Coding Classification with Scikit-Learn Part One

    • Random Forest - Coding Classification with Scikit-Learn Part Two

    • Random Forest Regression Overview

    • Random Forest Regression - Coding with Scikit-Learn Part One

    • Random Forest Regression - Coding with Scikit-Learn Part Two

    • Random Forest Regression - Coding with Scikit-Learn Part Three

  • 3

    Boosting

    • Introduction to Boosting

    • Boosting Theory - History and Motivation

    • Adaptive Boosting Theory - AdaBoost

    • AdaBoost - Coding with Scikit-Learn Part One

    • AdaBoost - Coding with Scikit-Learn Part Two

    • Gradient Boosting Theory

    • Coding Gradient Boosting with Scikit-Learn