Master Tree Ensemble Models

Tree Ensemble Models Course

Welcome to the most comprehensive online course on tree ensemble models, from decision trees to gradient boosting.

In this course, you will learn how decision trees, random forests, AdaBoost and gradient boosting machines work, and how XGBoost, LightGBM and CatBoost build on them to become the go-to models for tabular data.

You'll find intuitive explanations of how each algorithm works, the maths behind it, its strengths and limitations, and how to implement it in Python.

Why learn tree ensembles in depth?

Tree ensembles win most competitions and run in production across finance, insurance, healthcare, e-commerce and many other industries. Yet most data scientists import XGBoost, fit it with default parameters, and hope for the best.

Without understanding how these models work, it's easy to:

• Train models longer than needed.
• Tune hyperparameters blindly.
• Overfit without noticing.
• Pick a library that doesn't suit your data.

When you understand what happens under the hood, you can train faster, tune with intent, and choose the right model for the problem in front of you.

What will you learn in this course?

We start with decision trees, the building blocks of every ensemble, and work our way up to the most popular gradient boosting libraries.

Decision trees and random forests

You'll learn how decision trees choose their splits, why they overfit so easily, and how stopping criteria and pruning keep them in check. Next, you'll discover how bagging and random forests combine many unstable trees into a single, robust model.

Boosting

You'll learn how AdaBoost makes each new tree focus on past mistakes, and how this idea generalizes into gradient boosting. You'll see why fitting residuals is really following the gradient of a loss function, and how changing that loss lets you tackle regression, classification and prediction intervals.

XGBoost, LightGBM and CatBoost

Finally, you'll discover what makes each library different. You'll learn how XGBoost uses regularization and second-order gradients, how LightGBM gets its speed, and how CatBoost handles categorical variables. With this knowledge, you'll know which library to choose for your data, and how to tune it.

Course prerequisites

To make the most out of this course, you need to have:

• Basic knowledge of Python programming.
• Familiarity with machine learning concepts like training, testing and overfitting.
• Familiarity with Scikit-learn.

Who is this course for?

This course is for data scientists, machine learning engineers and students who use tree-based models and want to understand them in depth, so they can build better models and advance their careers.

To wrap-up

All topics include Python code examples in Jupyter notebooks that you can use for reference, practice, and reuse in your own projects.

The course comes with a 30-day money-back guarantee, so you can sign up today with no risk.

So what are you waiting for? Join us and start getting the most out of your tree ensemble models.