Getting Started with Automated Machine Learning (AutoML)

Instructor: Edureka

Access provided by New Apprenticeship

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

15 assignments¹

AI Graded see disclaimer

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 4 modules in this course

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.

What's included

14 videos6 readings5 assignments

14 videos Total 76 minutes

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

6 readings Total 75 minutes

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

5 assignments Total 39 minutes

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

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.

What's included

10 videos5 readings4 assignments

10 videos Total 58 minutes

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

5 readings Total 65 minutes

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

4 assignments Total 33 minutes

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

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.

What's included

13 videos4 readings4 assignments

13 videos Total 78 minutes

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

4 readings Total 55 minutes

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

4 assignments Total 33 minutes

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

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.