Grow Trees & Powerful Ensembles

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Grow Trees & Powerful Ensembles

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

Instructor: Hurix Digital

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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.

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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.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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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.

Build and prune CART models with stakeholder-ready visualizations

What's included

2 videos1 reading2 assignments1 ungraded lab

2 videos Total 12 minutes

CART Algorithm Fundamentals and Cost-Complexity Pruning 6 minutes
Building and Pruning CART Models with Scikit-learn 6 minutes

1 reading Total 12 minutes

Decision Tree Construction Mechanics and Pruning Strategies 12 minutes

2 assignments Total 24 minutes

Customer Churn Decision Tree Analysis 18 minutes
CART Construction and Pruning Assessment 6 minutes

1 ungraded lab Total 20 minutes

CART Model Construction and Pruning Implementation 20 minutes

Apply bagging, boosting, and stacking on the same dataset, compare metrics, and quantify ensemble lift over single models

What's included

3 videos2 readings1 assignment1 ungraded lab

3 videos Total 15 minutes

The Power of Ensemble Intelligence in Predictive Analytics 3 minutes
Implementing and Comparing Ensemble Methods 7 minutes
Building Ensemble Models with Performance Comparison 5 minutes

2 readings Total 18 minutes

Ensemble Method Architectures and Performance Metrics 11 minutes
Podcast: Strategic Ensemble Selection for Production Systems 7 minutes

1 assignment Total 7 minutes

Ensemble Methods Implementation and Performance Analysis 7 minutes

1 ungraded lab Total 20 minutes

Comprehensive Ensemble Method Implementation and Comparison 20 minutes

Evaluate computational cost vs. performance gain for each ensemble technique and recommend deployment feasibility

What's included

2 videos1 reading2 assignments

2 videos Total 10 minutes

Measuring and Comparing Computational Efficiency of Ensemble Methods 6 minutes
Computational Cost Analysis and Deployment Feasibility Assessment 5 minutes

1 reading Total 10 minutes

Computational Cost Framework for Ensemble Method Evaluation 10 minutes

2 assignments Total 55 minutes

Computational Cost Analysis and Deployment Decision-Making 35 minutes
Production Ensemble Deployment Analysis and Recommendation 20 minutes

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Instructor

Hurix Digital

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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.

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