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.

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)

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Getting Started with Automated Machine Learning (AutoML)

Dozent: Edureka

Bei enthalten

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4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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

Kompetenzen, die Sie erwerben

Kategorie: Applied Machine Learning
Kategorie: Scalability
Kategorie: Model Evaluation
Kategorie: Predictive Modeling
Kategorie: Artificial Intelligence and Machine Learning (AI/ML)
Kategorie: Artificial Intelligence
Kategorie: Feature Engineering
Kategorie: Machine Learning Algorithms
Kategorie: Data-Driven Decision-Making
Kategorie: Data Preprocessing
Kategorie: Machine Learning
Kategorie: Machine Learning Methods
Kategorie: Performance Metric
Kategorie: Performance Tuning
Kategorie: Automation
Kategorie: MLOps (Machine Learning Operations)

Werkzeuge, die Sie lernen werden

Kategorie: No-Code Development
Kategorie: Model Deployment
Kategorie: Scikit Learn (Machine Learning Library)

Wichtige Details

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März 2026

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In diesem Kurs gibt es 4 Module

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.

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.

Das ist alles enthalten

14 Videos6 Lektüren5 Aufgaben

14 Videos Insgesamt 76 Minuten

Course Introduction 6 Minuten
Why Automated Machine Learning Emerged 4 Minuten
Introduction to Automated Machine Learning 5 Minuten
Understanding AutoML in Practice 4 Minuten
Demonstration: Installing Python and Jupyter Notebook 5 Minuten
Machine Learning Workflow 6 Minuten
Evaluation, Selection, and Iteration in Practice 5 Minuten
Demonstration: Manual Decision-Making in a Standard ML Pipeline 8 Minuten
Categories of AutoML Frameworks 5 Minuten
Strengths and Trade-offs of Popular AutoML Approaches 6 Minuten
Introduction to H2O AutoML 5 Minuten
H2O AutoML Architecture 6 Minuten
Demonstration: Initializing an H2O AutoML Environment 5 Minuten
Demonstration: Running Your First AutoML Model with H2O 6 Minuten

6 Lektüren Insgesamt 75 Minuten

Course Syllabus 20 Minuten
Assessing When to Use Automated Machine Learning 15 Minuten
Revisiting the Classical Machine Learning Playbook 10 Minuten
Exploring the Broader AutoML Tooling Ecosystem 10 Minuten
H2O AutoML Readiness Checklist and Core Concepts 10 Minuten
Module Summary: Automated Machine Learning (AutoML) Essentials 10 Minuten

5 Aufgaben Insgesamt 39 Minuten

Knowledge Check: Introduction to Automated Machine Learning (AutoML) 6 Minuten
Knowledge Check: Understanding the Limits of Traditional Machine Learning Workflows 6 Minuten
Knowledge Check: AutoML Framework Landscape and Use Cases 6 Minuten
Knowledge Check: Introduction to H2O AutoML 6 Minuten
Knowledge Check: Automated Machine Learning (AutoML) Essentials 15 Minuten

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.

Das ist alles enthalten

10 Videos5 Lektüren4 Aufgaben

10 Videos Insgesamt 58 Minuten

Data and Feature Expectations in AutoML Systems 7 Minuten
What H2O AutoML Handles Automatically 5 Minuten
Demonstration: Automated Data Preprocessing using H2O 6 Minuten
Demonstration: Feature Diagnostics in H2O AutoML 7 Minuten
Demonstration: Variable Importance and Feature Optimization 6 Minuten
Model Selection as a Search Problem 5 Minuten
Ensembles as a Strategy for Model Selection 5 Minuten
Demonstration: Controlling Model Search in H2O AutoML 7 Minuten
Evaluation Metrics in H2O AutoML 5 Minuten
Metrics as Optimization Signals 5 Minuten

5 Lektüren Insgesamt 65 Minuten

Data-Centric AI and the Limits of Model-Centric Thinking in AutoML 10 Minuten
Feature Engineering and Selection Strategy Guide 15 Minuten
Designing Model Search as a Decision System 15 Minuten
Interpretation Guide for H2O AutoML Metrics 15 Minuten
Module Summary: Building Automated ML Pipelines with H2O AutoML 10 Minuten

4 Aufgaben Insgesamt 33 Minuten

Knowledge Check: Preparing Data for Automated Model Selection (AutoML) 6 Minuten
Knowledge Check: Model Selection in Automated Machine Learning (AutoML) 6 Minuten
Knowledge Check: Evaluation Metrics and Model Ranking in AutoML 6 Minuten
Knowledge Check: Building Automated ML Pipelines with H2O AutoML 15 Minuten

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.

Das ist alles enthalten

13 Videos4 Lektüren4 Aufgaben

13 Videos Insgesamt 78 Minuten

Understanding Hyperparameters and Their Impact 5 Minuten
Optimization Strategies in AutoML 5 Minuten
Demonstration: Hyperparameter Search and Evaluation in H2O 7 Minuten
Demonstration: Random Grid Search in H2O 6 Minuten
Selecting a Deployment Strategy 6 Minuten
Demonstration: Exporting AutoML Models for Production 7 Minuten
Demonstration: Running Real-Time Scoring with a MOJO 7 Minuten
Demonstration: Scaling MOJO Scoring to Production Patterns 5 Minuten
H2O Flow as an AutoML Control Center 5 Minuten
Demonstration: H2O Flow Interface and Workflow Navigation 5 Minuten
Demonstration: Data Import, Parsing, and Dataset Preparation in H2O Flow 6 Minuten
Demonstration: Configuring and Executing AutoML in H2O Flow 7 Minuten
Demonstration: Interpreting AutoML Results in H2O Flow 7 Minuten

4 Lektüren Insgesamt 55 Minuten

Advanced Considerations in Hyperparameter Optimization 15 Minuten
Productionization Reference Sheet 15 Minuten
Operationalizing AutoML with H2O Flow 15 Minuten
Module Summary: Optimizing and Operationalizing AutoML Systems 10 Minuten

4 Aufgaben Insgesamt 33 Minuten

Knowledge Check: Hyperparameter Optimization Fundamentals 6 Minuten
Knowledge Check: Deploying H2O AutoML Models with MOJO and POJO 6 Minuten
Knowledge Check: No-Code AutoML with H2O Flow 6 Minuten
Knowledge Check: Optimizing and Operationalizing AutoML Systems 15 Minuten

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.

Das ist alles enthalten

1 Video2 Aufgaben1 Unbewertetes Labor

Dozent

Edureka

159 Kurse 148.606 Lernende

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Häufig gestellte Fragen

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.

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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¹ Einige Aufgaben in diesem Kurs werden mit AI bewertet. Für diese Aufgaben werden Ihre Daten in Übereinstimmung mit Datenschutzhinweis von Courseraverwendet.