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Building, Evaluating, and Operationalizing ML Models · LearnSpace
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Building, Evaluating, and Operationalizing ML Models

Курс от Packt
Средний≈ 8.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will dive into the entire process of building, evaluating, and operationalizing machine learning (ML) models. Starting with data exploration, you'll learn how to select the right algorithms for regression and classification tasks and fine-tune them for optimal performance. As you progress, you'll gain hands-on experience with tools like Azure ML Studio, experimenting with model customization, feature engineering, and advanced algorithms such as XGBoost and Neural Networks. You'll also discover how to evaluate models, optimize performance, and deploy them effectively. The course offers practical demos, empowering you to implement everything from simple models to complex pipelines in ML workflows. Throughout the course, you'll explore model evaluation techniques, including cross-validation and performance metrics, and learn how to address issues like overfitting and model drift. You'll also engage with ML-Ops concepts, discovering how to structure scalable pipelines, automate workflows, and manage the lifecycle of your models. This course is perfect for those looking to gain real-world ML skills, especially those interested in Azure ML Studio and automated pipelines. Whether you’re starting with basic ML concepts or expanding your knowledge, this course provides a comprehensive guide. You’ll learn to make data-driven decisions while optimizing the end-to-end machine learning lifecycle. By the end, you'll be prepared to build and deploy models that are both efficient and scalable, while also staying on top of versioning and performance monitoring.

Навыки, которые вы освоите

Model OptimizationModel EvaluationModel TrainingClassification AlgorithmsModel DeploymentFine-tuning

Программа курса

3 модулей · 52 учебных материалов

01Building Machine Learning Models17 материалов

Building Machine Learning Models

Introduction to the Course 'Building, Evaluating, and Operationalizing ML Models'ЧтениеFull Specialization ResourcesЧтениеDEMO - Loading a Dataset and Exploring Basic Statistics in Azure ML StudioВидеоOverview of Common Machine Learning Algorithms for Regression and ClassificationВидео

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Building, Evaluating, and Operationalizing ML Models
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 8.6 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Венгерский

Часть программы вашего университета
Selecting the Best Algorithm Based on Data Type and Problem ComplexityВидео
Introduction to Ensemble Methods: Random Forests, Gradient Boosting MachinesВидео
DEMO - Selecting an Appropriate Model for a Dataset in Azure ML StudioВидео
Step-by-Step Process for Building a Model Using Pre-Built ModulesВидео
Customizing Models with Advanced Settings and HyperparametersВидео
DEMO - Building a Classification Model Using Azure ML StudioВидео
Feature Engineering: Creating New Features to Improve Model PerformanceВидео
Handling Missing Data and Categorical Variables Using Preprocessing TechniquesВидео
Using Cross-Validation to Assess Model GeneralizationВидео
Implementing Complex Algorithms: XGBoost, LightGBM, and Neural NetworksВидео
DEMO - Building an Advanced Model with Feature Engineering in Azure ML StudioВидео
Understanding and Validating Data Operations with an Instructor PersonaDIALOGUE
Building Machine Learning Models - AssessmentЗадание
02Model Evaluation and Optimization15 материалов

Model Evaluation and Optimization

Comparing Multiple Models to Select the Best PerformerВидеоEvaluating Model Performance Using Cross-Validation and Validation DatasetsВидеоUtilizing Model Performance MetricsВидеоClustering Evaluation: Silhouette Score, Adjusted Rand IndexВидеоDEMO - Performing Hyperparameter Tuning with Azure HyperDriveВидеоRegularization Techniques to Improve Model PerformanceВидеоIntroduction to Model EnsemblingВидеоModel Pruning and Simplification for EfficiencyВидеоOptimizing Model Inference Speed and Reducing LatencyВидеоDEMO - Running AutoML Experiment Using RegressionВидеоTracking Model Performance Over Time and Detecting Model DriftВидеоTechniques for Model Retraining and VersioningВидеоBest Practices for Managing Model Lifecycle and DeploymentВидеоModel Selection and Comparison TechniquesDIALOGUEModel Evaluation and Optimization - AssessmentЗадание
03Machine Learning Pipelines (ML-OPS)20 материалов

Machine Learning Pipelines (ML-OPS)

Overview of ML Pipelines and Their Importance in Automating WorkflowsВидеоDesigning and Building Reusable Pipelines in Azure ML StudioВидеоStructuring Pipelines for Scalability and EfficiencyВидеоOrganizing Pipeline Steps (Data Ingestion, Feature Engineering, Model Training)ВидеоUsing Azure ML Studio for Both Training and Deployment Pipeline CreationВидеоHow to Incorporate Custom Code into Azure ML PipelinesВидеоBest Practices for Versioning and Managing Dependencies in PipelinesВидеоConfiguring Environment Variables for Pipeline StepsВидеоConnecting External Resources (Databases, Cloud Storage) in the PipelineВидеоScheduling Pipeline Runs with Triggers (Time-Based, Event-Driven)ВидеоDEMO - Building a Custom Pipeline with Python Scripts in Azure ML StudioВидеоADVANCED - Integrating Multiple Pipeline Components for Complex WorkflowsВидеоADVANCED - Handling Failures and Retries in PipelinesВидеоADVANCED - Using PipelineParameters for Dynamic InputsВидеоDEMO - Using PythonScriptStep to Run Custom Python Scripts within PipelinesВидеоConclusion to the Course 'Building, Evaluating, and Operationalizing ML Models'ЧтениеDesigning, Automating, and Scaling Machine Learning Pipelines in Azure MLDIALOGUEMachine Learning Pipelines (ML-OPS) - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание