As machine learning adoption grows across industries, automated machine learning (AutoML) platforms are becoming essential for accelerating model development and improving productivity. This course equips you with the practical skills to build, evaluate, optimize, and deploy ML models using H2O AutoML which is one of the most widely adopted open-source automated machine learning platforms. Using H2O, you can start producing results from day one.
Getting Started with Automated Machine Learning (AutoML)
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您将学到什么
Explain why AutoML emerged and how it reduces scaling limits in traditional ML workflows
Control and interpret model search using constraints, ensembles, and leaderboard signals
Apply structured hyperparameter optimization using metric-based comparison and search controls
Deploy H2O AutoML models by selecting and exporting MOJO or POJO artifacts for production
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4个模块
Develop a clear mental model of AutoML and its emergence from the scaling limits of manual ML workflows. You will define AutoML as a structured experimentation system and understand H2O AutoML’s execution architecture. Finally, you will run and interpret your first baseline AutoML workflow.
涵盖的内容
14个视频6篇阅读材料5个作业
Explore the operational core of AutoML: data readiness, model search, and metric-driven evaluation. You will assess data requirements, recognize automation limits, and interpret leaderboards and feature importance as decision evidence. You will frame model selection as a search problem, evaluate ensemble performance, and use metrics as optimization signals.
涵盖的内容
10个视频5篇阅读材料4个作业
Shift to production-ready AutoML systems. You will conduct structured hyperparameter searches, compare configurations using metrics, and apply controls such as early stopping and checkpointing for reproducible tuning. You will then deploy models via MOJO/POJO, implement scalable scoring patterns, and execute the lifecycle in H2O Flow for inspection.
涵盖的内容
13个视频4篇阅读材料4个作业
Integrate the complete AutoML lifecycle through an end-to-end workflow design and final assessment. You will translate course concepts into a coherent solution covering data preparation, model selection, evaluation strategy, and operational considerations. You will justify decisions using metric evidence, trade-off analysis, and established best practices.
涵盖的内容
1个视频2个作业1个非评分实验室
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常见问题
AutoML automates the end-to-end machine learning workflow — including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model ranking. It enables users to build production-quality ML models efficiently, even without extensive programming or data science expertise.
This course focuses on H2O AutoML, one of the most widely adopted open-source AutoML platforms in industry. You will also work with H2O Flow, a no-code visual interface for building and evaluating ML models without writing any code.
Yes. This course is built around a follow-along, demo-driven learning model. Each concept is taught through step-by-step video demonstrations using H2O AutoML and H2O Flow that you can replicate on your own setup. You are encouraged to pause, rewind, and practice alongside each demo at your own pace. The course also includes environment setup guidance so you can configure your local H2O installation from the very first lesson.
更多问题
提供助学金，
¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。