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Getting Started with Automated Machine Learning (AutoML) · LearnSpace
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Getting Started with Automated Machine Learning (AutoML)

Курс от Edureka
Начальный≈ 11.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Model EvaluationModel OptimizationData PreprocessingAutomationPredictive ModelingApplied Machine LearningArtificial Intelligence and Machine Learning (AI/ML)No-Code DevelopmentData-Driven Decision-MakingMachine Learning MethodsArtificial IntelligenceModel TrainingMachine Learning AlgorithmsMachine LearningData ValidationData QualityScikit Learn (Machine Learning Library)Feature EngineeringMLOps (Machine Learning Operations)Model Deployment

Программа курса

4 модулей · 73 учебных материалов

01Automated Machine Learning (AutoML) Essentials26 материалов

Introduction to Automated Machine Learning (AutoML)

Course IntroductionВидеоCourse SyllabusЧтениеWhy Automated Machine Learning EmergedВидеоIntroduction to Automated Machine LearningВидео

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Edureka

Преподаватель курса

Getting Started with Automated Machine Learning (AutoML)
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Обучение на Coursera

≈ 11.6 ч

4 модулей

Язык: Английский

Часть программы вашего университета
Understanding AutoML in PracticeВидео
Demonstration: Installing Python and Jupyter NotebookВидео
Assessing When to Use Automated Machine LearningЧтение
Knowledge Check: Introduction to Automated Machine Learning (AutoML)Задание

Understanding the Limits of Traditional Machine Learning Workflows

Machine Learning WorkflowВидеоEvaluation, Selection, and Iteration in PracticeВидеоDemonstration: Manual Decision-Making in a Standard ML PipelineВидеоRevisiting the Classical Machine Learning PlaybookЧтениеKnowledge Check: Understanding the Limits of Traditional Machine Learning WorkflowsЗадание

AutoML Framework Landscape and Use Cases

Understanding the AutoML Framework LandscapeВидеоStrengths and Trade-offs of Popular AutoML ApproachesВидеоExploring the Broader AutoML Tooling EcosystemЧтениеKnowledge Check: AutoML Framework Landscape and Use CasesЗадание

Introduction to H2O AutoML

Introduction to H2O AutoMLВидеоH2O AutoML ArchitectureВидеоDemonstration: Initializing an H2O AutoML EnvironmentВидеоDemonstration: Running Your First AutoML Model with H2OВидеоH2O AutoML Readiness Checklist and Core ConceptsЧтениеKnowledge Check: Introduction to H2O AutoMLЗадание

Module Wrap-Up and Assessment

Module Summary: Automated Machine Learning (AutoML) EssentialsЧтениеAutoML Foundations CheckpointDIALOGUEKnowledge Check: Automated Machine Learning (AutoML) EssentialsЗадание
02Building Automated ML Pipelines with H2O AutoML20 материалов

Preparing Data for Automated Model Selection (AutoML)

Data and Feature Expectations in AutoML SystemsВидеоWhat H2O AutoML Handles AutomaticallyВидеоDemonstration: Automated Data Preprocessing using H2OВидеоDemonstration: Feature Diagnostics in H2O AutoMLВидеоDemonstration: Variable Importance and Feature OptimizationВидеоData-Centric AI and the Limits of Model-Centric Thinking in AutoMLЧтениеFeature Engineering and Selection Strategy GuideЧтениеKnowledge Check: Preparing Data for Automated Model Selection (AutoML)Задание

Model Selection in Automated Machine Learning (AutoML)

Model Selection as a Search ProblemВидеоEnsembles as a Strategy for Model SelectionВидеоDemonstration: Controlling Model Search in H2O AutoMLВидеоDesigning Model Search as a Decision SystemЧтениеKnowledge Check: Model Selection in Automated Machine Learning (AutoML)Задание

Evaluation Metrics and Model Ranking in AutoML

Evaluation Metrics in H2O AutoMLВидеоMetrics as Optimization SignalsВидеоInterpretation Guide for H2O AutoML MetricsЧтениеKnowledge Check: Evaluation Metrics and Model Ranking in AutoMLЗадание

Module Wrap-Up and Assessment

Module Summary: Building Automated ML Pipelines with H2O AutoMLЧтениеAutoML Execution Framework CheckpointDIALOGUEKnowledge Check: Building Automated ML Pipelines with H2O AutoMLЗадание
03Optimizing and Operationalizing AutoML Systems22 материалов

Hyperparameter Optimization Fundamentals

Understanding Hyperparameters and Their ImpactВидеоOptimization Strategies in AutoMLВидеоDemonstration: Hyperparameter Search and Evaluation in H2OВидеоDemonstration: Random Grid Search in H2OВидеоAdvanced Considerations in Hyperparameter OptimizationЧтениеKnowledge Check: Hyperparameter Optimization FundamentalsЗадание

Deploying H2O AutoML Models with MOJO and POJO

Selecting a Deployment StrategyВидеоDemonstration: Exporting AutoML Models for ProductionВидеоDemonstration: Running Real-Time Scoring with a MOJOВидеоDemonstration: Scaling MOJO Scoring to Production PatternsВидеоProductionization Reference SheetЧтениеKnowledge Check: Deploying H2O AutoML Models with MOJO and POJOЗадание

No-Code AutoML with H2O Flow

H2O Flow as an AutoML Control CenterВидеоDemonstration: H2O Flow Interface and Workflow NavigationВидеоDemonstration: Data Import, Parsing, and Dataset Preparation in H2O FlowВидеоDemonstration: Configuring and Executing AutoML in H2O FlowВидеоDemonstration: Interpreting AutoML Results in H2O FlowВидеоOperationalizing AutoML with H2O FlowЧтение

Module Wrap-Up and Assessment

Module Summary: Optimizing and Operationalizing AutoML SystemsЧтениеAutoML Deployment Readiness CheckpointDIALOGUEKnowledge Check: Optimizing and Operationalizing AutoML SystemsЗадание
04Course Wrap-Up and Assessment5 материалов

Course Conclusion

AI-Powered Retail Product Recommendation OptimizationDIALOGUECapstone Project: End-to-End AutoML System using H2OЛабораторнаяPredicting Patient Outcomes with H2O FlowЗаданиеEnd Course Knowledge Check: AutoML - Automated Model Selection and TuningЗаданиеCourse SummaryВидео
Knowledge Check: No-Code AutoML with H2O FlowЗадание