Improve the accuracy and reliability of your machine learning models by mastering ensemble techniques. In this intermediate-level course, you’ll learn why combining multiple models can outperform any single algorithm and how to design, select, and apply the right ensemble approach for different tasks. You’ll work through three core ensemble methods—bagging, boosting, and random forests—using Java in a Jupyter Notebook environment. Starting with the fundamentals of decision trees, you’ll progress from theory to practice, exploring bootstrap sampling, hard/soft voting, and the bias–variance trade-offs that influence ensemble performance. Each lesson combines focused videos, scenario-based discussions, AI-graded labs, and a capstone project, guiding you to build and evaluate ensembles on real datasets.
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Empfohlene Erfahrung
Was Sie lernen werden
Explain the core principles of ensemble learning and describe when and why combining diverse models improves predictive accuracy.
Implement bagging and boosting algorithms in Java within a Jupyter Notebook, tuning key parameters for optimal performance.
Build, tune, and evaluate random forest models for classification and regression, interpret features, and compare results with ensemble methods.
Kompetenzen, die Sie erwerben
- Kategorie: Sampling (Statistics)
- Kategorie: Decision Tree Learning
- Kategorie: Learning Styles
- Kategorie: Program Evaluation
- Kategorie: Predictive Modeling
- Kategorie: Data Preprocessing
- Kategorie: Program Implementation
- Kategorie: Java
- Kategorie: Feature Engineering
- Kategorie: Classification Algorithms
- Kategorie: Supervised Learning
- Kategorie: Random Forest Algorithm
- Kategorie: Model Evaluation
- Kategorie: Applied Machine Learning
- Kategorie: Machine Learning
- Kategorie: Jupyter
Wichtige Details
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Dezember 2025
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In diesem Kurs gibt es 3 Module
This module explains the core idea behind ensemble learning—combining multiple models to achieve higher predictive accuracy and stability than any single model. Learners explore how ensembles reduce bias and variance, review real-world use cases, and implement voting classifiers to see the performance gains firsthand.
Das ist alles enthalten
4 Videos2 Lektüren1 peer review
This module teaches how to increase model accuracy by reducing variance with bagging and reducing bias with boosting. Learners practice bootstrap sampling, implement bagging in Java using Jupyter, and build a boosting model including AdaBoost to see how sequential learning corrects errors.
Das ist alles enthalten
3 Videos1 Lektüre1 peer review
This module covers decision tree fundamentals and shows how random forests combine many trees through feature bagging and averaging to create powerful, stable predictors. Learners build, tune, and evaluate random forest models in Java, interpreting feature importance and comparing results to single-tree models.
Das ist alles enthalten
4 Videos1 Lektüre1 Aufgabe2 peer reviews
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