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.
Improve Accuracy with ML Ensemble Methods
Improve Accuracy with ML Ensemble Methods
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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
- Java
- Feature Engineering
- Classification Algorithms
- Program Implementation
- Program Evaluation
- Supervised Learning
- Decision Tree Learning
- Random Forest Algorithm
- Model Evaluation
- Data Preprocessing
- Learning Styles
- Jupyter
- Predictive Modeling
- Sampling (Statistics)
- Machine Learning
- Applied Machine Learning
- 技能部分已折叠。显示 9 项技能，共 16 项。
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
积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
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
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