Online Course
Gradient Boosting Made Easy: XGBoost, LightGBM & Friends

Name: Gradient Boosted Trees in Python: XGBoost, LightGBM & CatBoost
Price: 36 USD

Master Gradient Boosted Trees with XGBoost, LightGBM, and CatBoost—three of the most powerful libraries in modern machine learning. This course will sharpen your modeling skills and enhance your ability to build accurate, high-performance solutions for real-world business and AI-driven applications.

4.8

862 reviews on

Start for free

122 students already enrolled

Skill level:

Intermediate

Duration:

2 hours

Lessons (2 hours)

CPE credits:

4

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

The fundamentals of Gradient Boosted Trees (GBT).
Implementing Gradient Boosted Trees from scratch to build intuition.
Techniques for hyperparameter tuning using grid & random search strategies.
Best practices for applying XGBoost, LightGBM and Catboost GBT Models.
Deploying GBT models into production for real-world business applications
Strategies for integrating GBT Models into broader data pipelines & AI workflows

Topics & tools

Gradient Boosted TreesMachine LearningAIMachine and Deep LearningMath & StatisticsMLOpsPython

Your instructor

Samson Afolabi

Data Scientist & Analytics Leader | Team Lead C Analytics at Free2move | Data Engineering & AI

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Mathematics

Ask Samson a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 4 Field of Study: Information Technology
Delivery Method: QAS Self Study

This comprehensive course offers a deep dive into Gradient Boosted Trees (GBT), one of the most effective machine learning techniques for tabular data. You’ll begin with a clear introduction to the principles behind Gradient Boosted Trees, understanding how boosting improves model accuracy by combining weak learners. From there, the course guides you through the practical implementation of Gradient Boosted Trees, laying a strong foundation for working with leading libraries in the field.

You'll gain hands-on experience with XGBoost, LightGBM, and CatBoost, learning how to leverage their unique strengths, optimize their usage for different types of data, and integrate them into your data science workflow. Dedicated modules cover real-world application of each library, showing you how to build, train, and evaluate high-performance models efficiently.

The course also includes a detailed section on hyperparameter tuning, where you'll explore techniques like manual tuning, grid search and randomized search fine-tune your models for maximum accuracy and generalization.

Finally, you'll learn how to deploy Gradient Boosted Trees models into production environments, ensuring that your solutions can deliver real business value at scale. Whether you're improving customer segmentation, forecasting, or building recommendation engines, this course will equip you with the practical tools and knowledge to apply Gradient Boosted Trees effectively in your data science and AI projects.

Prerequisites

Good knowledge of Python programming
Familiarity with the scikit-learn library
Understanding of basic machine learning concepts e.g. Linear Regression
Eagerness to learn and apply new tools
Basic knowledge of Bash/command line usage

Advanced preparation

None

Curriculum

46 lessons 25 exercises 1 exam

1. First things first

8 min

8 min

Introduction Free

Why This Course? Free

Setting Up Your Environment Free
2. Introduction to Gradient Boosted Trees

21 min

21 min

Introduction Free

Decision Trees Free

Some Common Tree Terms Free

Exercise Free

Ensemble Methods - Boosting Free

Gradient Descent Free

Exercise Free

Gradient Boosted Trees Free

A Simple Example Free

Exercise Free
3. Implementation: Gradient Boosted Trees

17 min

17 min

Introduction

Introduction to Gradient Boosted Tree Libraries

Comparing XGBoost, LightGBM, and CatBoost

Exercise

When to Use Which Libraries

Another Example : Gradient Boosted Model

Exercise

Feature Importance with Gradient Boosted Trees
4. XGBoost in Practice

16 min

16 min

Introduction to XGBoost

Data Preparation for XGBoost

Exercise

Training a XGBoost Model

Example 1: XGBoost in Practice

Example 2: XGBoost in Practice
5. LightGBM in Practice

16 min

16 min

Introduction to LightGBM

Core Concepts of LightGBM

Exercise

Data Preparation for LightGBM

Training a Model with LightGBM

Exercise

Example 1 : LightGBM in Practice (Regression)

Example 2 : LightGBM in Practice (Classification)
6. CatBoost in Practice

12 min

12 min

Why CatBoost?

Key Features of CatBoost

Exercise

Data Preparation and Training with CatBoost

Example 1: CatBoost in Practice

Example 2: CatBoost in Practice
7. Hyperparameter Tuning

23 min

23 min

Introduction

Hyperparameters vs. Parameters

Why Hyperparameter Tuning is Important

Exercise

Key Hyperparameters in Gradient Boosted Trees

Tuning Techniques

Exercise

Example 1 : Hyperparameter Tuning (Regression + Grid Search)

Example 2: Hyperparameter Tuning (Classification + Random Search)

Best Practices & Pitfalls

Exercise
8. Bonus: Deployment Readiness

11 min

11 min

Introduction

Saving & Loading Models

Environment Reproducibility

Exercise

Monitoring GBT Models in Production

CI/CD for Gradient Boosted Trees

Exercise
9. Conclusion

6 min

6 min

Conclusion
10. Course exam

60 min

60 min

Course exam

Free lessons

1.1 Introduction

4 min

1.2 Why This Course?

3 min

1.3 Setting Up Your Environment

1 min

2.1 Introduction

1 min

2.2 Decision Trees

4 min

2.3 Some Common Tree Terms

3 min

Start for free

365 Data Science taught me how to understand, interpret, and visualize data—skills I’ve applied to reduce program costs, improve data quality, and streamline reporting in my NGO role. Using techniques like confidence intervals, I optimized commodity redistribution, cutting frequency and saving resources. I’ve advanced from Excel to SQL and Python, tackling real-life problems as a data engineering intern. 365’s structured courses have made my career transition smoother and boosted my impact in both roles.

Arowosegbe V. I.

Before 365: Medical Lab Scientist | APIN Public Health Initiatives

After 365: Business analyst | Meesho

See all reviews

9 in 10

people walk away career-ready

with practical data and AI skills.

In July 2021, I started my career transition into data analytics with 365 Data Science. The beginner-friendly, flexible, and affordable courses made it possible to learn Python, SQL, Tableau, and Excel while moving internationally. The supportive instructors, challenging assignments, and real portfolio projects prepared me for interviews and helped me land my current role in pharmaceutical logistics. 365 made a difficult career transition possible, and I highly recommend it to anyone starting a new career in data science.

Lifang K.

Before 365: Volunteer Management Analyst | American Red Croos

After 365: BI Analyst | Marken

See all reviews

$29,000

average salary increase

after moving to an AI and data science career

I joined 365 Data Science to strengthen my skills in data science. Starting with the Python Bootcamp, I quickly learned the programming skills I needed for my job. The platform also helped me refresh my math and statistics and gain a solid foundation in machine learning. The courses are easy to follow, practical, and directly applicable to my work. I highly recommend 365 to anyone wanting to learn programming and data science—it’s a resource I recommend to friends, family, and colleagues.