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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您将学到什么
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.
您将获得的技能
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
该课程共有3个模块
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.
涵盖的内容
4个视频2篇阅读材料1次同伴评审
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.
涵盖的内容
3个视频1篇阅读材料1次同伴评审
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.
涵盖的内容
4个视频1篇阅读材料1个作业2次同伴评审
提供方
从 Machine Learning 浏览更多内容
状态：免费试用
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状态：免费试用Board Infinity
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