When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Grow Trees & Powerful Ensembles

4 days left! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

Grow Trees & Powerful Ensembles

This course is part of Statistical Inference & Predictive Modeling Foundations Specialization

Instructor: Hurix Digital

Included with

Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Interpretability vs Performance: Choose explainable trees or high-performing ensembles based on business context and stakeholder needs.
Stability as Validation: Model consistency across data variations matters as much as accuracy for reliable production use.
Ensemble Selection Strategy: Select bagging, boosting, or stacking based on data characteristics and computational limits.
Resource-Conscious Deployment: Balance accuracy gains with operational cost, infrastructure limits, and real-time requirements.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Statistical Inference & Predictive Modeling Foundations Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

Ready to transform your data science expertise with the most powerful tree-based modeling techniques? This Short Course was created to help data analysis professionals accomplish advanced predictive modeling using decision trees and ensemble methods.

By completing this course, you'll master CART model construction, ensemble method implementation, and deployment feasibility assessment. You'll gain hands-on experience with scikit-learn, XGBoost, and real-world performance optimization scenarios that directly impact business decisions. By the end of this course, you will be able to: Build and prune CART models with stakeholder-ready visualizations Evaluate model stability through bootstrapping techniques Compare bagging, boosting, and stacking performance gains Assess computational trade-offs for production deployment This course is unique because it bridges the gap between theoretical ensemble methods and practical deployment constraints, ensuring your models are both performant and operationally feasible. To be successful in this project, you should have a background in Python programming and basic machine learning concepts.