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Ensemble Machine Learning in Python: Random Forest, AdaBoost

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

Created byLazy Programmer Team, Lazy Programmer Inc.

Last updated 2/2026

English

What you'll learn

Understand and derive the bias-variance decomposition
Understand the bootstrap method and its application to bagging
Understand why bagging improves classification and regression performance
Understand and implement Random Forest
Understand and implement AdaBoost

Course content

11 sections • 45 lectures • 5h 47m total length

Outline and Motivation5:40
Where to get the Code and Data12:03
All Data is the Same3:15
Plug-and-Play2:11
How to Succeed in This Course8:42

Bias-Variance Key Terms6:37
Bias-Variance Trade-Off3:09
Bias-Variance Decomposition3:32
Polynomial Regression Demo18:07
K-Nearest Neighbor and Decision Tree Demo6:32
Explore ensemble methods through a hands-on demo of k-nearest neighbor and decision trees, illustrating high bias low variance versus low bias high variance in regression and classification.
Cross-Validation as a Method for Optimizing Model Complexity4:26
Suggestion Box3:10

Random Forest Algorithm8:54
Random Forest Regressor7:05
Use a random forest regressor on a house price dataset with numerical and binary features, normalization, and a log-transformed target; compare via cross-validation to linear regression and a decision tree.
Random Forest Classifier4:56
Apply the Random Forest classifier to a mushrooms dataset to predict poisonous versus edible, using label-encoded categorical features and cross-validation to compare models, with Random Forest achieving the highest accuracy.
Random Forest vs Bagging Trees3:47
Compare random forest and bagging trees as the number of trees grows, showing test error convergence and less overfitting in random forest across datasets.
Implementing a "Not as Random" Forest4:12
Implement a pseudo random forest by training base models on bootstrap samples with a fixed feature subset and align inputs for prediction.
Connection to Deep Learning: Dropout2:38

How to Code by Yourself (part 1)15:54
How to Code by Yourself (part 2)9:23
Proof that using Jupyter Notebook is the same as not using it12:29
Show that using Jupiter notebook makes no difference to Python code; code runs identically in Jupiter notebook or console, and print statements aid debugging.
Python 2 vs Python 34:38
Choose your Python version for the course, with code updated to Python 3, and learn the key differences between Python 2 and 3, including print, range, and division behavior.

Requirements

Calculus (derivatives)
Numpy, Matplotlib, Sci-Kit Learn
K-Nearest Neighbors, Decision Trees
Probability and Statistics (undergraduate level)
Linear Regression, Logistic Regresion

Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years.

Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

This course is all about ensemble methods.

We've already learned some classic machine learning models like k-nearest neighbor and decision tree. We've studied their limitations and drawbacks.

But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?

In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of.

In particular, we will study the Random Forest and AdaBoost algorithms in detail.

To motivate our discussion, we will learn about an important topic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.

We'll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.

Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Calculus (derivatives)
Probability
Object-oriented programming
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations
Simple machine learning models like linear regression and decision trees

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

Who this course is for:

Understand the types of models that win machine learning contests (Netflix prize, Kaggle)
Students studying machine learning
Professionals who want to apply data science and machine learning to their work
Entrepreneurs who want to apply data science and machine learning to optimize their business
Students in computer science who want to learn more about data science and machine learning
Those who know some basic machine learning models but want to know how today's most powerful models (Random Forest, AdaBoost, and other ensemble methods) are built

Ensemble Machine Learning in Python: Random Forest, AdaBoost

What you'll learn

Explore related topics

Course content

Get Started5 lectures • 32min

Bias-Variance Trade-Off7 lectures • 46min

Bootstrap Estimates and Bagging6 lectures • 38min

Random Forest6 lectures • 32min

AdaBoost7 lectures • 37min

Background Review1 lecture • 10min

Appendix / FAQ Intro1 lecture • 4min

Setting Up Your Environment (FAQ by Student Request)3 lectures • 42min

Extra Help With Python Coding for Beginners (FAQ by Student Request)4 lectures • 42min

Effective Learning Strategies for Machine Learning (FAQ by Student Request)4 lectures • 1hr

Requirements

Description

Who this course is for: