Machine Learning with Decision Trees and Random Forests

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

with Nikola Pulev

4.8/5

(664)

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

1 hour of content 4436 students

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What you get:

1 hour of content
27 Interactive exercises
11 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Machine Learning with Decision Trees and Random Forests

A course by Nikola Pulev

Start for Free

What you get:

1 hour of content
27 Interactive exercises
11 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

$99.00

Lifetime access

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Start for Free

What you get:

1 hour of content
27 Interactive exercises
11 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Master Decision Trees and Random Forests to elevate your data analysis skills to the next level
Fully grasp the inner workings of Decision Trees and Random Forests as well as their practical application
Understand the pros and cons of Decision Trees and Random Forests algorithms to make informed decisions in model selection
Build and optimize predictor models using Decision Trees and Random Forests and learn why they are indispensable when it comes to understanding the problem at hand
Integrate essential math concepts with hands-on Python programming skills
Develop the skills to independently plan, execute, and deliver a complete ML project from start to finish

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.

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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

Curriculum

1. Introduction to Decision Trees and Random Forests

1 Lesson 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?
5 min
2. Setting up the Environment

2 Lessons 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 Read now
1 min
Installing the relevant packages
3 min
3. Decision Trees

10 Lessons 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?
4 min
The Concept of Decision Trees
3 min
Decision Trees in Machine Learning
5 min
Decision Trees: Pros and Cons
7 min
Practical Example: The Iris Dataset
2 min
Practical Example: Creating a Decision Tree
6 min
Practical Example: Plotting the Tree
7 min
Decision Tree Metrics Intuition: Gini Impurity
6 min
Decision Tree Metrics: Information Gain
2 min
Tree Pruning: Dealing with Overfitting
4 min
4. Random Forests

7 Lessons 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
3 min
Bootstrapping
4 min
From Bootstrapping to Random Forests
3 min
Random Forest in Code – Glass Dataset
9 min
Census Data and Income – Preprocessing
9 min
Training the Decision Tree
3 min
Training the Random Forest
4 min

Topics

TheoryPythonMachine LearningProgrammingDecision TreesRandom ForestMachine and Deep Learning

Tools & Technologies

Course Requirements

You need to complete an introduction to Python before taking this course
Basic skills in statistics, probability, and linear algebra are required
It is highly recommended to take the Machine Learning in Python course first
You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Intermediate

Aspiring data scientists and ML engineers

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Nikola Pulev

Course Creator at

6 Courses

1775 Reviews

26789 Students

Nikola Pulev is a Natural Sciences graduate from the University of Cambridge (UK) turned data science practitioner and a course instructor at 365 Data Science. Nikola has a strong passion for mathematics, physics, and programming. Over the years, he has taken part in multiple national and international competitions, where he has won numerous awards. One of Nikola’s most notable achievements so far is his silver medal from the International Physics Olympiad.