Free Udemy Course 2025: Certification in AZURE ML - Free Udemy Course [100% Off]

DescriptionTake the Next Step in Your Azure and Machine Learning Journey!Whether you're an aspiring data scientist, cloud engineer, software developer, or business leader, this course will equip you with the skills to harness Azure’s powerful machine learning ecosystem for scalable, real-world AI solutions. Learn how Azure ML Studio, AutoML, Python, and integrated Azure services are transforming data preparation, model training, deployment, and monitoring—enabling faster, smarter, and more impactful decision-making.Guided by hands-on projects and real-world use cases, you will:Master foundational machine learning concepts and Azure ML workflows applied to real business scenarios.Gain hands-on experience collecting, managing, and preparing data using Azure Blob Storage, Data Lake, and ML Studio.Learn to train, optimize, and deploy models using AutoML, the Azure ML SDK, and scalable compute resources.Explore industry applications in predictive analytics, recommendation systems, sentiment analysis, and AI-powered automation.Understand best practices for MLOps, workflow automation, security, compliance, and cost optimization in Azure ML environments.Position yourself for a competitive advantage by developing in-demand skills at the intersection of cloud computing, artificial intelligence, and data analytics.The Frameworks of the Course• Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises—designed to help you deeply understand how to apply Azure Machine Learning for building, training, deploying, and managing AI solutions in the cloud.• The course includes industry-specific case studies, Azure ML tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to develop, optimize, and operationalize machine learning models using Azure’s powerful ecosystem.• In the first part of the course, you’ll learn the basics of machine learning, Azure Cloud Services, and how Azure ML enhances scalability, automation, and integration in AI workflows.• In the middle part of the course, you will gain hands-on experience using Azure ML Studio, AutoML, Jupyter Notebooks, Python SDK, and integrated services like Azure Data Lake and Power BI to collect, process, and analyze data, train models, and create interactive dashboards.• In the final part of the course, you will explore MLOps automation, cost optimization, security and compliance strategies, and real-world applications across industries. All your queries will be addressed within 48 hours, with full support throughout your learning journey.Course Content:Part 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. Introduction to Azure and Machine Learning1.1. Basics of Machine Learning - Key Concepts and Use Cases1.2. Overview of Azure Cloud Services1.3. Introduction to Azure Machine Learning Services1.4. Key Features of Azure ML Studio1.5. Hands-On Activity - Set up an Azure account and explore the Azure portal, Navigate Azure ML Studio and create a workspace1.6. Conclusion of Introduction to Azure and Machine LearningModule 2. Data Management on Azure ML2.1. Data Storage and Management with Azure Blob Storage2.2. Data Preparation and Feature Engineering in Azure ML Studio2.3. Introduction to Azure Data Lake for Big Data Analytics2.4. Importing and Managing Datasets in Azure ML2.5. Hands - On Activity - Upload datasets to Azure Blob Storage and Connect them to o Azure ML, Perform basic data preprocessing using Azure ML Designer2.6. Conclusion of Data Management on Azure MLModule 3. Building and Training Models on Azure ML3.1. Overview of Azure ML Designer for No - Code ML Development3.2. Using Jupyter Notebooks and SDK for Code - Based Model Development3.3. Automated ML (AutoML) in Azure3.4. Custom Model Training with Azure ML Compute Instances and Clusters3.5. Hands-On Activity - Train a model using AutoML in Azure ML Studio, Develop a custom ML model using Python and Azure ML SDK.3.6. Conclusion of Building and Training Models on Azure MLModule 4. Model Optimization and Evaluation4.1. Hyperparameter Tuning with Azure ML Hyperdrive4.2. Evaluating Model Performance Metrics4.3. Cross-Validation and Model Selection Techniques4.4. Model Explainability with Azure Interpretability Toolkit4.5. Hands-On Activity - Optimize a model using Hyperdrive, Evaluate and visualize model performance in Azure ML Studio4.6. Conclusion of Model Optimization and EvaluationModule 5. Deploying Machine Learning Models with Azure ML5.1. Creating Inference Pipelines in Azure ML5.2. Real Time vs Batch Inference on Azure5.3. Model Deployment to Azure Kubernetes Service(AKS) or Azure Container Instances.5.4. Endpoint Configuration and Authentication5.5. Hands-On Activity - Deploy a trained model to an Azure ML endpoint, Test the deployed model with sample inputs5.6. Conclusion of Deploying Machine Learning Models with Azure MLModule 6. Integrating Azure ML with Other Azure Services6.1. Data Analytics with Azure Synapse and Power BI6.2. Monitoring and Logging with Azure Monitor6.3. Workflow Automation with Azure Logic Apps6.4. Building AI-Powered Applications with Cognitive Services6.5. Hands-On Activity - Create a dashboard in Power BI integrating predictions from an Azure ML model6.6. Conclusion of Integrating Azure ML with other Azure ServicesModule 7. MLOps and Workflow Automation7.1. Introduction to MLOps and CI/CD for Machine Learning7.2. Azure Pipelines for ML Workflow Automation7.3. Managing Model Versioning and Lifecycles7.4. Monitoring and Retraining Deployed Models7.5. Hands-On Activity - Implement an automated ML pipeline using Azure DevOps7.6. Conclusion of MLOps and Workflow AutomationModule 8. Security, Compliance, and Cost Optimization8.1. Data Security in Azure ML - Role-Based Access Control (RBAC)8.2. Compliance with Industry Standards (GDPR, HIPAA, etc.)8.3. Cost Optimization Strategies for Azure ML Workloads8.4. Azure ML Pricing Models and Billing Practices8.5. Hands - On Activity - Set up RBAC roles for a project in Azure ML, Estimate and monitor costs using Azure Cost Management8.6. Conclusion of Security, Compliance and Cost OptimizationModule 9. Real-World Use Cases and Applications9.1. Financial Services - Fraud Detection and Risk Management9.2. Healthcare - Predictive Analytics and Diagnostics9.3. Retail - Demand Forecasting and Personalization9.4. Manufacturing - Predictive Maintenance9.5. Hands-On Activity - Solve a domain-specific problem using Azure ML Services9.6. Conclusion of Real-World Use Cases and ApplicationsPart 2: Capstone Project.