Online Course
Machine Learning with Decision Trees and Random Forests

Name: Machine Learning Course: Decision Trees & Random Forests
Price: 36 USD

with Nikola Pulev

Master Decision Trees and Random Forests: from theoretical foundations to practical applications

4.9

808 reviews on

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4545 students already have enrolled

Skill level:

Intermediate

Duration:

1 hour

Lessons (1 hour)

Practice exams (6 minutes)

CPE credits:

3

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited:

certificate

What You Learn

Master Decision Trees and Random Forests for data analysis.
Understand the pros and cons of these ML algorithms.
Build and optimize predictive models using these techniques.
Integrate math concepts with hands-on Python programming.
Plan, execute, and deliver a complete ML project independently.

Topics & tools

theorypythonmachine learningprogrammingdecision treesrandom forestmachine and deep learning

Your instructor

Nikola Pulev

Data Scientist | Course Creator at 365 Data Science | University of Cambridge Physics graduate

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, Natural Sciences, Physics

Media and recognition

Silver Medal, International Physics Olympiad
National and international awards in mathematics and physics competitions

Ask Nikola a question

Course Overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 3 Field of Study: Information Technology
Delivery Method: QAS Self Study

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.

Prerequisites

Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
Intermediate Python skills are required.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

20 lessons 27 exercises 2 exams

1. Introduction to Decision Trees and Random Forests

5 Min

In this introductory section, you will get to know your instructor, go over the contents of the course, and discover why mastering ML with Decision Trees and Random forests is essential for progressing your predictive analytics skillset.

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

4 Min

Section 2 prepares you for the practical part of the course by guiding you through the process of installing all relevant Python packages.

Setting up the environment Free

Installing the relevant packages Free
3. Decision Trees

46 Min

This is the main section of the course where we will use visual examples to make sense of the concept of decision trees. We will cover the advantages and disadvantages of this method and find out what goes into building decision tree models. You will also learn about a popular technique known as tree pruning. In order to apply your newly found skills, you will be diving into a practical example of how to create decision trees with sklearn.

What Is a Tree in Computer Science? Free

Exercise

The Concept of Decision Trees Free

Decision Trees in Machine Learning Free

Exercise

Decision Trees: Pros and Cons Free

Exercise

Practical Example: The Iris Dataset Free

Practical Example: Creating a Decision Tree Free

Practical Example: Plotting the Tree

Decision Tree Metrics Intuition: Gini Impurity

Exercise

Decision Tree Metrics: Information Gain

Exercise

Tree Pruning: Dealing with Overfitting

Exercise
4. Random Forests

35 Min

The final section of this course is dedicated to the random forest algorithm. We will learn about bootstrapping and bagged decision trees – all steps towards the creation of a random forest. It is important to understand the distinction in applications between decision trees and random forests, so this is included as well. Finally, we conclude this section and the course with a comprehensive case study. The first half of our practical example is dedicated to showing you how to implement random forests in sklearn. After that, we will model a person’s salary based on various census features. We will create both a decision tree and a random forest model for this dataset and compare the performance of each.

Random Forest as Ensemble Learning

Exercise

Bootstrapping

From Bootstrapping to Random Forests

Exercise

Random Forest in Code – Glass Dataset

Census Data and Income – Preprocessing

Training the Decision Tree

Training the Random Forest

Practice exam
5. Course exam

50 Min

Course exam

Free preview

1.1 What does the course cover?

5 min

2.1 Setting up the environment

1 min

2.2 Installing the relevant packages

3 min

3.1 What Is a Tree in Computer Science?

4 min

3.3 The Concept of Decision Trees

3 min

3.4 Decision Trees in Machine Learning

5 min

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