What is Automated Machine Learning (AutoML)?

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.

What AutoML tools will I learn in this course?

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.

Is this a follow-along course?

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.

Do I need coding experience to take this course?

Basic familiarity with Python and data concepts is helpful but not required. Module 3 includes a dedicated lesson on H2O Flow, a no-code visual interface that allows you to execute the full AutoML lifecycle without writing a single line of code.

What types of ML models will I build?

You will build classification and regression models using real-world datasets. H2O AutoML automatically trains and compares multiple algorithms including GBM, XGBoost, Deep Learning, and Stacked Ensembles and ranks them on a leaderboard for your evaluation.

What is the H2O AutoML leaderboard?

The leaderboard is a ranked list of all models trained during an AutoML run, sorted by performance metrics. You will learn to interpret leaderboard signals to make informed model selection decisions rather than blindly trusting the top-ranked model.

What are MOJO and POJO, and will I learn to use them?

MOJO (Model Object, Optimized) and POJO (Plain Old Java Object) are H2O’s model export formats for production deployment. In Module 3, you will learn to export models as MOJO artifacts and implement both real-time and batch scoring patterns.

H2O Flow is a web-based, no-code interface for running AutoML experiments. In Lesson 3.3, you will use Flow to import data, configure AutoML runs, and interpret results, all through a point-and-click visual workflow without writing code.

Who is this course designed for?

The course targets undergraduate students in engineering, computer science, and data science, as well as working professionals who want to modernize their ML workflow using automated approaches. Business analysts interested in data-driven decision-making will also benefit.

Will I receive a certificate upon completion?

Yes. Upon completing all graded assessments, you will earn a Coursera Course Certificate from Edureka that you can add to your LinkedIn profile, resume, or CV.

When will I have access to the lectures and assignments?

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.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Getting Started with Automated Machine Learning (AutoML)

位教师：Edureka

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深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

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

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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.

Throughout this course, you’ll explore the full AutoML lifecycle and discover how automated pipelines are replacing the trial-and-error approach to model development. Each concept is reinforced through step-by-step video demonstrations using H2O AutoML and H2O Flow that you can follow along and practice at your own pace. By the end of this course, you’ll be able to: • Explain what AutoML is, run baseline experiments, and interpret H2O leaderboards for model selection. • Prepare data for automated model selection, diagnose feature quality, and prevent data leakage. • Control model search using constraints and ensembles, and evaluate models using metrics like RMSE, AUC, and Logloss. • Optimize hyperparameters with structured grid search strategies and deploy models via MOJO artifacts for real-time and batch scoring. • Execute the full AutoML lifecycle through H2O Flow, a no-code visual interface, without writing a single line of code. This course is designed for a diverse audience: undergraduate students in engineering, computer science, and data science, working professionals modernizing their ML workflows, business analysts exploring data-driven decision-making, and anyone looking to gain practical machine learning skills with an automated, structured approach. Prior familiarity with basic data concepts and Python is helpful, though the course includes a dedicated no-code module using H2O Flow for learners without programming experience. Take the first step toward automated machine learning mastery and build the skills needed to deliver production-ready ML solutions using H2O AutoML.

单元详情

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

14个视频总计76分钟

Course Introduction6分钟
Why Automated Machine Learning Emerged4分钟
Introduction to Automated Machine Learning5分钟
Understanding AutoML in Practice4分钟
Demonstration: Installing Python and Jupyter Notebook5分钟
Machine Learning Workflow6分钟
Evaluation, Selection, and Iteration in Practice5分钟
Demonstration: Manual Decision-Making in a Standard ML Pipeline8分钟
Categories of AutoML Frameworks5分钟
Strengths and Trade-offs of Popular AutoML Approaches6分钟
Introduction to H2O AutoML5分钟
H2O AutoML Architecture6分钟
Demonstration: Initializing an H2O AutoML Environment5分钟
Demonstration: Running Your First AutoML Model with H2O6分钟

6篇阅读材料总计75分钟

Course Syllabus20分钟
Assessing When to Use Automated Machine Learning15分钟
Revisiting the Classical Machine Learning Playbook10分钟
Exploring the Broader AutoML Tooling Ecosystem10分钟
H2O AutoML Readiness Checklist and Core Concepts10分钟
Module Summary: Automated Machine Learning (AutoML) Essentials10分钟

5个作业总计39分钟

Knowledge Check: Automated Machine Learning (AutoML) Essentials15分钟
Knowledge Check: Introduction to Automated Machine Learning (AutoML)6分钟
Knowledge Check: Understanding the Limits of Traditional Machine Learning Workflows6分钟
Knowledge Check: AutoML Framework Landscape and Use Cases6分钟
Knowledge Check: Introduction to H2O AutoML6分钟

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

10个视频总计58分钟

Data and Feature Expectations in AutoML Systems7分钟
What H2O AutoML Handles Automatically5分钟
Demonstration: Automated Data Preprocessing using H2O6分钟
Demonstration: Feature Diagnostics in H2O AutoML7分钟
Demonstration: Variable Importance and Feature Optimization6分钟
Model Selection as a Search Problem5分钟
Ensembles as a Strategy for Model Selection5分钟
Demonstration: Controlling Model Search in H2O AutoML7分钟
Evaluation Metrics in H2O AutoML5分钟
Metrics as Optimization Signals5分钟

5篇阅读材料总计65分钟

Data-Centric AI and the Limits of Model-Centric Thinking in AutoML10分钟
Feature Engineering and Selection Strategy Guide15分钟
Designing Model Search as a Decision System15分钟
Interpretation Guide for H2O AutoML Metrics15分钟
Module Summary: Building Automated ML Pipelines with H2O AutoML10分钟

4个作业总计33分钟

Knowledge Check: Building Automated ML Pipelines with H2O AutoML15分钟
Knowledge Check: Preparing Data for Automated Model Selection (AutoML)6分钟
Knowledge Check: Model Selection in Automated Machine Learning (AutoML)6分钟
Knowledge Check: Evaluation Metrics and Model Ranking in AutoML6分钟

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

13个视频总计78分钟

Understanding Hyperparameters and Their Impact5分钟
Optimization Strategies in AutoML5分钟
Demonstration: Hyperparameter Search and Evaluation in H2O7分钟
Demonstration: Random Grid Search in H2O6分钟
Selecting a Deployment Strategy6分钟
Demonstration: Exporting AutoML Models for Production7分钟
Demonstration: Running Real-Time Scoring with a MOJO7分钟
Demonstration: Scaling MOJO Scoring to Production Patterns5分钟
H2O Flow as an AutoML Control Center5分钟
Demonstration: H2O Flow Interface and Workflow Navigation5分钟
Demonstration: Data Import, Parsing, and Dataset Preparation in H2O Flow6分钟
Demonstration: Configuring and Executing AutoML in H2O Flow7分钟
Demonstration: Interpreting AutoML Results in H2O Flow7分钟

4篇阅读材料总计55分钟

Advanced Considerations in Hyperparameter Optimization15分钟
Productionization Reference Sheet15分钟
Operationalizing AutoML with H2O Flow15分钟
Module Summary: Optimizing and Operationalizing AutoML Systems10分钟

4个作业总计33分钟

Knowledge Check: Optimizing and Operationalizing AutoML Systems15分钟
Knowledge Check: Hyperparameter Optimization Fundamentals6分钟
Knowledge Check: Deploying H2O AutoML Models with MOJO and POJO6分钟
Knowledge Check: No-Code AutoML with H2O Flow6分钟

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.