Are you ready to master one of machine learning’s most powerful and interpretable algorithms? This course will guide you through the complete journey of understanding, building, and evaluating decision tree models using Java, the enterprise-standard programming language. You’ll start by exploring the core concepts, how decision trees partition data, why splitting criteria such as entropy and the Gini index matter, and when decision trees outperform other algorithms. From there, you’ll move into hands-on implementation, using industry-standard tools like Weka’s intuitive GUI and Java API along with Smile’s high-performance library to develop, tune, and deploy models. Through practical exercises, you’ll learn to configure hyperparameters, balance rapid prototyping with production-ready design, and apply robust model evaluation techniques such as confusion matrices, cross-validation, and key performance metrics.
Build & Evaluate Decision Trees for ML
您将学到什么
Explain decision tree fundamentals including tree structure, splitting criteria, and how recursive partitioning builds predictive models.
Build decision tree classifiers using Weka GUI and Java API, implement models with Smile, and configure hyperparameters for optimal performance.
Evaluate decision tree models using confusion matrices, accuracy metrics, cross-validation techniques, and interpret results to assess model quality.
您将获得的技能
- Machine Learning Algorithms
- Supervised Learning
- Applied Machine Learning
- Feature Engineering
- Machine Learning
- MLOps (Machine Learning Operations)
- Java
- Predictive Modeling
- Tree Maps
- Decision Tree Learning
- Data Preprocessing
- Classification Algorithms
- Technical Communication
- Model Evaluation
- Machine Learning Software
- Algorithms
- 技能部分已折叠。显示 9 项技能，共 16 项。
要了解的详细信息
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January 2026
1 项作业
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- 获得可共享的职业证书
该课程共有3个模块
Explore decision tree foundations including tree structure, classification mechanics, splitting criteria like entropy and Gini index, and how recursive partitioning creates predictive models for machine learning applications.
涵盖的内容
4个视频2篇阅读材料1次同伴评审
Build decision tree classifiers using Weka's GUI and Java API, then explore Smile library for modern implementations. Configure hyperparameters, train models on real datasets, and export trained models.
涵盖的内容
3个视频1篇阅读材料1次同伴评审
Evaluate decision tree performance using confusion matrices, accuracy metrics, precision, recall, and F1-scores. Apply cross-validation techniques to assess model generalization. Learn to interpret results and identify overfitting.
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
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
提供方
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状态：免费试用
状态：免费试用
状态：免费试用
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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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