Machine Learning with Decision Trees and Random Forests

Guiding you through the fundamentals of decision trees and random forests. You will learn how the trees are constructed both theoretically and in practice using sklearn.

with Nikola Pulev

Course Overview

Decision trees and random forests are tools that every data scientist or machine learning practitioner should be familiar with. Famous for producing good predictors, these methods are also indispensable when it comes to understanding the problem at hand, as well as visualizing and communicating your results. That’s why we have prepared this course for you. The first part features a thorough explanation of the workings of decision trees, how to code and visualize them with sklearn, and the pros and cons you should consider. Then we will build on the concept of a single decision tree to produce the random forest algorithm. Finally, we will cap it all off with a practical example implementing both decision trees and random forests in Python to predict a person’s income based on census data.

20 High Quality Lessons

1 Practical Tasks

1 Hours of Video

Certificate of Achievement

Skills you will gain

machine learningprogrammingpythontheory

What You'll Learn

This practical course introduces you to the inner workings of decision trees and random forests. You will learn how a tree is constructed and how the concept of decision trees extends to random forests, as well as how these methods can be applied in several different practical examples.

Understand decision trees

Visualize and interpret the tree

Gini impurity and information gain

Pruning

Extend decision trees to random forests

Bootstrapping

Curriculum

Introduction to Support Vector Machines Free

1 Lesson 5 Min

What does the course cover? Free
Setting up the Environment Free

2 Lessons 3 Min

Setting up the environment Free Installing the relevant packages Free
Decision Trees

10 Lessons 46 Min

What Is a Tree in Computer Science? Free The Concept of Decision Trees Free Decision Trees in Machine Learning Free Decision Trees: Pros and Cons Free Practical Example: The Iris Dataset Free Practical Example: Creating a Decision Tree Free Practical Example: Plotting the Tree Decision Tree Metrics Intuition: Gini Impurity Decision Tree Metrics: Information Gain Tree Pruning: Dealing with Overfitting
Random Forests

7 Lessons 35 Min

Random Forest as Ensemble Learning Bootstrapping From Bootstrapping to Random Forests Random Forest in Code – Glass Dataset Census Data and Income – Preprocessing Training the Decision Tree Training the Random Forest

“The ability to interpret a model’s results is indispensable in Machine Learning. That’s where decision trees come into play. The decision tree model is relatively simple and easy to understand – both characteristics that make it a great foundation for ML enthusiasts to grasp and visualize basic concepts.”

Nikola Pulev

Silver medal at Physics Olympiad

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