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)

Instructeur : Edureka

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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

Compétences que vous acquerrez

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

Outils que vous découvrirez

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

Détails à connaître

Certificat partageable

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Il y a 4 modules dans ce cours

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.

Inclus

14 vidéos6 lectures5 devoirs

14 vidéos 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 lectures 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 devoirs 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.

Inclus

10 vidéos5 lectures4 devoirs

10 vidéos 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 lectures 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 devoirs 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.

Inclus

13 vidéos4 lectures4 devoirs

13 vidéos 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 lectures 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 devoirs 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.

Inclus

1 vidéo2 devoirs1 laboratoire non noté

Instructeur

Edureka

157 Cours 144 987 apprenants

Offert par

Edureka

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Foire Aux Questions

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 Certains travaux de ce cours sont notés par l'IA. Pour ces travaux, vos Données internes seront utilisées conformément à Notification de confidentialité de Coursera.