Program profile · official sources
Gradient Boosting Made Easy: XGBoost, LightGBM & Friends
365 Data Science's 'Gradient Boosting Made Easy: XGBoost, LightGBM & Friends' is an intermediate-level online course that explores advanced machine learning techniques for tabular data. Instructed by Samson Afolabi, the 2-hour provider headline duration covers the implementation of Gradient Boosted Trees using popular libraries like XGBoost, LightGBM, and CatBoost. Learners are guided through decision trees, hyperparameter tuning, and deploying models into production. Earning the provider-issued course certificate requires both completing the course and passing the associated course exam with a score of 60% or higher. The provider states the course awards 4 CPE credits. Required prerequisites include Python programming, scikit-learn, basic machine learning concepts, and command-line usage. Pricing observations in the United States on 3 October 2026 indicate platform subscription access rather than a fixed per-course fee.
Facts checked 3 Oct 2026
- Price
- See pricing
Price observation recorded 3 Oct 2026 (not a current price): As shown in the United States on 3 October 2026, the 365 pricing page displays a Free tier at $0/month and a Self-study price of $29/month billed annually, shown against a $36/month reference price. The FAQ separately states $29/month full access. A Lifetime 'Pay once' plan lacks a numeric amount. USD is confirmed in the page HTML metadata. These are platform subscription quotes, not a fixed course-completion total. Certificates are included with the Self-study learning plan.
- Time
- 2 hours (provider headline; module and exam timings listed separately)
- Level
- Intermediate
- Credential
- Provider-issued course certificate
Verifiable credential with Credential ID and Credential Link; sharable on LinkedIn/resumes; downloadable upon course completion and passing the exam. Certificates are included with the Self-study learning plan.
- Prerequisites
- Required: 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. Recommended advanced preparation: None.
- Format
- Online Course
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
Sources ↗
No accepted numeric price in this registry. See the sourced pricing details below.
See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.
What’s covered
- First things first
- Introduction to Gradient Boosted Trees
- Implementation: Gradient Boosted Trees
- XGBoost in Practice
- LightGBM in Practice
- CatBoost in Practice
- Hyperparameter Tuning
- Bonus: Deployment Readiness
- Conclusion
- Course exam
Prerequisites: Required: 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. Recommended advanced preparation: None.
What you actually do
- First things first : 8 min; Section 1 · Source
- Introduction to Gradient Boosted Trees : 21 min; Section 2 · Source
- Implementation: Gradient Boosted Trees : 17 min; Section 3 · Source
- XGBoost in Practice : 16 min; Section 4 · Source
- LightGBM in Practice : 16 min; Section 5 · Source
- CatBoost in Practice : 12 min; Section 6 · Source
- Hyperparameter Tuning : 23 min; Section 7 · Source
- Bonus: Deployment Readiness : 11 min; Section 8 · Source
- Conclusion : 6 min; Section 9 · Source
- Course exam : 60 min; Section 10 · Source
What the credential is
- Credential type
- Provider-issued course certificate · 365datascience.com / gradient-boosting-made-easy-xgboost-lightgbm-friends
- Credential
- Course Certificate · 365datascience.com / certificates
- Issuer
- 365 Data Science · 365datascience.com / certificates
- Sharing
- Verifiable credential with Credential ID and Credential Link; sharable on LinkedIn/resumes; downloadable upon course completion and passing the exam. Certificates are included with the Self-study learning plan. · 365datascience.com / certificates
- Exam requirement
- Requires both course completion and passing the associated course exam with a score of 60% or above. Certificates are included with the Self-study learning plan. · 365datascience.com / certificates
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