Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

The complete Azure Machine learning course - 2026 Edition

Name: The complete Azure Machine learning course - 2026 Edition
Rating: 3.7 (200 reviews)

Master Machine Learning with Azure ML Studio – Build, Train & Deploy AI Models Using No-Code & Python.

Role Play

Created byCyberdefense Learning

Last updated 2/2026

English

English [Auto],

What you'll learn

Learn about supervised, unsupervised, and reinforcement learning, key concepts like training data, models, predictions, and real-world applications.
Navigate and utilize Azure ML Studio's tools, including Designer, Notebooks, Automated ML, and Model Management.
Load, clean, transform, and engineer features using Azure ML Studio to optimize model performance.
Use Azure ML Studio’s visual interface and custom Python scripts to create, train, and evaluate machine learning models.
Apply hyperparameter tuning, cross-validation, and automated ML techniques to enhance model accuracy and efficiency.
Learn different model deployment strategies, including real-time inference, batch inference, and Edge deployments using Azure Kubernetes Service (AKS) and Azure
Create reusable machine learning workflows using Azure ML Pipelines for training, evaluation, and deployment automation.
Set up CI/CD pipelines, automate model retraining, monitor model drift, and ensure security and compliance with Azure DevOps.
Work with GPT, DALL·E, Stable Diffusion, and Codex, fine-tune AI models, and apply responsible AI principles for fairness and transparency.
Work through multiple demos, labs, and real-world projects to gain practical experience in Azure Machine Learning.
Learners preparing for Microsoft AI certifications like AI-102 , AI-900 etc.

Course content

8 sections • 113 lectures • 16h 32m total length

Definition and overview of machine learning (ML)4:35
Types of machine learning Supervised, Unsupervised, Reinforcement Learning.7:03
Key concepts Training data, features, labels, models, predictions6:43
Real-world applications of ML in industries such as healthcare & finance9:13
Challenges in machine learning Overfitting, underfitting, data quality, and in7:03
Introduction to Azure ML Studio and its capabilities for building, training, a6:30
Overview of the Azure Machine Learning workspace Datasets, experiments, models6:06
Key components Designer, Notebooks, Automated ML, and Model Management5:36
Key features Visual interface, AutoML, integration with Azure services (Data F5:18
Scalability and flexibility with Azure Compute and storage options5:00
Collaboration and sharing Team-based development and version control4:56
Benefits Faster experimentation, model deployment, and continuous learning5:20
Creating an Azure account4:26
Exploring Azure Cloud Interface and Services Part-110:54
Navigate the Azure portal and free tier credits to explore Windows and Linux VMs, storage, databases, AI services, and networking features.
Exploring Azure Cloud Interface and Services Part-212:32
Exploring Azure Cloud Interface and Services Part-310:57
Creating Azure ML Studio10:47
Exploring key features and benefits of Azure ML Studio15:33
Overview of resource management Workspaces, compute resources, and storage acc10:49
Connecting to data sources and Azure services.9:59
Module 1 Quiz: Introduction to Machine Learning and Azure
Explaining Azure Machine Learning Studio to a Business Stakeholder

Importing datasets from various sources local files, Azure Blob Storage, SQL d6:32
Exploring dataset statistics and visualizing data distribution7:10
Understanding data types (numerical, categorical, text, image)6:03
Identifying and handling missing data ( Null, Nan Values )5:30
Outlier detection and treatment strategies5:59
Removing duplicates and irrelevant issues3:40
Correcting data types and formatting issues4:15
DEMO - Cleaning a dataset by handling missing values and outliers in ML Studio16:42
Splitting datasets into training, validation, and test sets5:44
Random sampling and stratified sampling techniques5:19
Data normalization and scaling techniques MinMax scaling, Standardization Zscore5:31
Handling imbalanced datasets and using oversampling & undersampling techniques6:05
DEMO Splitting and normalizing a dataset in Azure ML Studio17:38
Creating new features through transformations (logarithmic, polynomial features)2:50
Introduction to feature selection Choosing relevant features for model training.6:16
Encoding categorical variables (One-Hot Encoding, Label Encoding)5:39
Learn how to encode categorical variables using one-hot and label encoding, comparing unordered and ordered categories, their memory and computation impacts, and when to apply each technique.
Feature Selection and Transformation5:50
Data Transformation & Augmentation9:06
Exploring ML Studio Designer and Setting up an Experiment29:19
Azure Machine Learning studio designer by building an automobile price prediction experiment, from data cleaning to training a regression model, scoring, and evaluation on a compute instance.
Module 2 Quiz: Data Basics and Preprocessing
Data Scientist Meeting: Preprocessing a Faulty Dataset for Machine Learning
DEMO Loading a dataset and exploring basic statistics in Azure ML Studio13:37

Overview of common machine learning algorithms for regression classification9:23
Selecting the best algorithm based on data type and problem complexity8:31
Introduction to ensemble methods Random Forests, Gradient Boosting Machines6:57
DEMO Selecting an appropriate model for a dataset in Azure ML Studio13:27
Step-by-step process for building a model using pre-built modules.8:41
Customizing models with advanced settings and hyperparameters.6:23
DEMO Building a classification model using Azure ML Studio20:02
Feature engineering Creating new features to improve model performance.6:46
Explore feature engineering as a crucial step that creates and transforms features—such as polynomial terms, interactions, and domain-specific transforms—to boost model performance and generalization in Azure ML.
Handling missing data and categorical variables using preprocessing technique7:00
Using cross-validation to assess model generalization.8:02
Implementing complex algorithms such as XGBoost, LightGBM, and Neural Network7:36
DEMO Building an advanced model with feature engineering in Azure ML Studio19:47
Module 3 Quiz: Building Machine Learning Models
Reviewing Your Machine Learning Model with the Product Owner

Comparing multiple models to select the best performer.8:24
Evaluating model performance using cross-validation and validation datasets.8:02
Utilizing model performance metrics10:48
Clustering evaluation Silhouette Score, Adjusted Rand Index.8:49
Evaluate clustering models in unsupervised learning using the silhouette score and adjusted rand index to assess cluster cohesion, separation, and alignment with ground truth when available.
DEMO Performing hyperparameter tuning with Azure HyperDrive12:17
Regularization techniques to improve model performance8:54
Introduction to model ensembling4:05
Model pruning and simplification for efficiency4:10
Optimizing model inference speed and reducing latency.7:41
Demo Running AutoML experiment using Regression13:43
Tracking model performance over time and detecting model drift2:39
Techniques for model retraining and versioning.4:10
Best practices for managing model lifecycle and deployment4:06
Module 4 Quiz: Model Evaluation and Optimization
Presenting and Defending Your Model Optimization Strategy

Overview of ML pipelines and their importance in automating workflows.8:05
Designing and building reusable pipelines in Azure ML Studio4:02
Structuring pipelines for scalability and efficiency.2:12
Organizing pipeline steps (data ingestion, feature engineering, model training2:46
Using Azure ML Studio for both training and deployment pipeline creation.2:44
How to incorporate custom code into Azure ML Pipelines.5:24
Best practices for versioning and managing dependencies in pipelines.4:52
Configuring environment variables for pipeline steps4:41
Connecting external resources (databases, cloud storage) in the pipeline.4:41
Scheduling pipeline runs with triggers (time-based, event-driven)4:23
DEMO Building a custom pipeline with Python scripts in Azure ML Studio16:41
ADVANCED- Integrating multiple pipeline components for complex workflows.4:53
ADVANCED- Handling failures and retries in pipelines5:24
ADVANCED - Using PipelineParameters for dynamic inputs.5:30
DEMO Using PythonScriptStep to run custom Python scripts within pipelines.8:24
Module 5 Quiz: Machine Learning Pipelines
Technical Review Meeting: Justify Your Azure ML Pipeline Design

1. Parallel processing and scaling ML workloads10:16
Distributed training with TensorFlowPyTorch on Azure Compute Clusters9:17
Learn to deploy distributed training on Azure compute clusters with TensorFlow and PyTorch, using TF distribute strategy, mirrored strategy, and DDP, plus auto scaling and pipelines for scalable, cost-efficient training.
DEMO Distributed training with TensorFlowPyTorch on Azure Compute Clusters10:11
Demonstrates distributed training with TensorFlow or PyTorch on Azure compute clusters by preparing a training script, configuring tf.distribute, and submitting a command job in Azure Machine Learning.
Choosing between real-time and batch inference.11:17
Serverless Model deployments with Azure functions10:12
DEMO Deploying a real-time ML model on AKS13:50
Role-Based Access Control (RBAC) and API security.10:43
Master role-based access control and API security for Azure ML deployments, implementing authentication, authorization, key management, IP whitelisting, rate limiting, and audit logging to safeguard models and data.
Logging and alerting with Azure Monitor and Application Insights.10:08
DEMO Logging and alerting with Azure Monitor and Application Insights21:14
Introduction to Advance deployment strategies10:57
Explore advanced deployment strategies for machine learning models, including A/B testing and canary deployment in Azure ML, to minimize risk and ensure smooth, data-driven rollouts.
Deploying ML models on Edge devices with Azure IoT.9:04
Model optimization with ONNX for efficient inference.8:03
? Module 6 Quiz: Advanced Model Training and Deployment
Scaling ML Deployments for a Critical Product Launch

Importance of MLOps in modern AI applications.10:20
Key differences between DevOps and MLOps.4:36
Challenges in operationalizing ML models.6:04
Automating ML workflows with Azure DevOps & GitHub Actions.6:35
Infrastructure as Code (IaC) for ML environments.5:42
Explore how infrastructure as code automates and scales machine learning environments on Azure. Compare Terraform and Bicep for reproducible, version-controlled deployments in ML workflows.
Data encryption, compliance (GDPR, HIPAA), and security best practices.8:43
DEMO Role Based Access in Azure ML8:35
Quiz Description: MLOps (Machine Learning Operations) (Module 7)
Navigating a Production ML Deployment Review

Understanding Generative AI : What is it?10:28
Understanding Generative AI : Types of Generative Models13:34
Understanding Generative AI : Popular Generative AI Models4:31
Lab Demo : Using GPT in Azure ML-117:31
Lab Demo : Using GPT in Azure ML-212:49
Lab Demo : Generating AI-Generated Art with DALL·E19:56
Fine-Tuning Generative AI Models ; Why Fine-Tuning is Needed ?9:11
Techniques for Fine-Tuning GPT & Other Models8:04
Explore supervised fine tuning, reinforcement learning with human feedback, and parameter efficient methods like Lora and adapters to tailor AI models for tasks, dialogue, and domain-specific use cases.
Lab Demo : Creating a Domain-Specific Chatbot17:23
Lab Demo : Enhancing Text Generation for Custom Use Cases20:05
Ethical Considerations in Generative AI : Challenges in Generative AI11:12
Bias in AI models: How training data influences outputs (e.g., gender, racial, or political bias).
AI hallucination: When AI generates false but confident-sounding information.
Misinformation & Deepfakes: Risks in media, social influence, and cybercrime.
Techniques for Responsible AI Development10:27
Bias Detection & Fairness Analysis in Azure ML.
Explainability tools (SHAP, LIME) to make AI more transparent.
Microsoft’s Responsible AI Framework: Ensuring compliance with EU AI Act, GDPR.
Lab Demo : Auditing Bias in AI Models9:25
Use Fairlearn & InterpretML to analyze model bias.
Adjust datasets and retrain models to improve fairness.
Lab Demo : Using Explainable AI in Azure ML8:32
Apply SHAP (SHapley Additive exPlanations) to interpret why a model made a certain prediction.
Improve trust & regulatory compliance by making AI decisions transparent.
Module 8 Quiz: Exploring Generative AI with Azure ML Studio
Evaluating Fine-Tuning Strategy for a Healthcare Chatbot

Requirements

Familiarity with Python syntax, data types, and simple programming concepts will be helpful but is not mandatory.
Some awareness of cloud services, particularly Microsoft Azure, will be useful but not required.
Concepts like averages, probability, and basic algebra will help in understanding machine learning models, but the course will explain these as needed.
You'll need an Azure account to access Azure Machine Learning Studio and complete hands-on exercises.
Since Azure ML Studio is cloud-based, you’ll need a stable internet connection.
The course runs entirely in Azure Machine Learning Studio, so no local installations are needed.
If you don’t have an Azure account, you can sign up for a free tier to access cloud-based ML tools.

Description

The Cloud-Scale Intelligence Revolution

Machine Learning is no longer a laboratory experiment—it is the engine of modern industry. From predicting financial market shifts to real-time cybersecurity threat detection, the demand for Data-Driven Decision Making is absolute. However, the gap between a "working model" and a "production system" is massive.

This course is designed to bridge that gap using Microsoft Azure Machine Learning Studio. You will move past the complexities of infrastructure setup and into the world of cloud-based efficiency, mastering the entire machine learning lifecycle from raw data to global deployment.

Foundations & Real-World Architecture

We begin by grounding your technical skills in the core logic of AI, ensuring you understand not just how to build, but why specific architectures succeed:

The ML Spectrum: Master Supervised, Unsupervised, and Reinforcement Learning.
Overcoming Engineering Hurdles: Learn tactical solutions for Overfitting, data quality issues, and the critical challenge of Model Interpretability.
Industry Deep Dives: See how these models function in high-stakes environments like Healthcare, Finance, and Retail.

Hands-On Workspace Orchestration

Azure ML Studio is your new command center. You will gain hands-on expertise in navigating the interface and managing a professional workspace:

Data Engineering at Scale: Master preprocessing techniques, including missing value handling, one-hot encoding, and Principal Component Analysis (PCA).
Feature Engineering: Learn to transform raw data into high-performance features that drive model accuracy.
AutoML Mastery: Leverage Automated Machine Learning to optimize models with minimal manual effort, allowing you to focus on high-level strategy.

Advanced Model Training & Optimization

You will explore a diverse library of algorithms and advanced training techniques to ensure elite performance:

Algorithm Mastery: From Regression and Classification to Clustering and Neural Networks.
Ensemble Methods: Learn to combine the power of multiple models using Random Forests and Gradient Boosting.
Hyperparameter Tuning: Use Azure’s compute clusters to find the "sweet spot" for your model’s configuration automatically.

The MLOps Frontier: CI/CD & Automation

A professional model is never "finished." You will learn to treat Machine Learning like modern software by implementing MLOps (Machine Learning Operations):

Azure ML Pipelines: Build end-to-end automated workflows for data ingestion, training, and evaluation.
CI/CD Integration: Use Azure DevOps and GitHub Actions to version your models and automate retraining when data shifts occur.
Model Governance: Implement Role-Based Access Control (RBAC) and monitoring tools to detect and fix "Model Drift" before it affects your business.

Generative AI & The Future of Azure

Stay at the bleeding edge with a dedicated dive into the world of Foundation Models:

Azure OpenAI Services: Hands-on with GPT, DALL·E, and Codex.
Fine-Tuning for Industry: Learn to adapt massive AI models for specific, domain-heavy applications.
Responsible AI: Implement bias detection and explainability frameworks to ensure your AI is ethical and transparent.

Certification Readiness: DP-100 & AI-102

This curriculum is precision-engineered to align with the highest industry standards. By the end of this course, you will be fully prepared to sit for:

Microsoft Certified: Azure Data Scientist Associate (DP-100)
Microsoft Certified: Azure AI Engineer Associate (AI-102)

The Outcome
You will walk away with a portfolio of automated, cloud-deployed machine learning systems and the architectural mindset required to lead AI initiatives in any enterprise.
Stop building models. Start engineering intelligence. Let's begin.

Who this course is for:

If you’re new to ML and want a structured, hands-on introduction using Azure Machine Learning Studio, this course will provide step-by-step guidance.
Learners preparing for Exam AI-102: Microsoft Certified: Azure AI Engineer Associate
If you have some knowledge of ML but want to scale your models using Azure’s cloud-based ML tools, this course will help you learn model training, deployment, and automation.
If you work with data and want to transition into machine learning and AI, this course will teach you how to build, optimize, and deploy ML models efficiently in Azure ML Studio.
you're an Azure user, cloud engineer, or solutions architect, this course will teach you how to integrate Azure ML with cloud-based services for AI-driven solutions.
If you’re a software developer or Python programmer looking to automate machine learning workflows and deploy AI solutions, this course will provide the skills you need.
If you're interested in MLOps, CI/CD for ML models, and automated retraining, this course covers end-to-end model lifecycle management in Azure ML.
If you work in healthcare, finance, retail, cybersecurity, or any data-driven industry, this course will show you how machine learning can solve real-world business problems.

The complete Azure Machine learning course - 2026 Edition

What you'll learn

Explore related topics

Course content

Introduction to Machine Learning and Azure21 lectures • 2hr 39min

Data Basics and Preprocessing21 lectures • 2hr 49min

Module 3: Building Machine Learning Models13 lectures • 2hr 3min

Module 4: Model Evaluation and Optimization14 lectures • 1hr 38min

Module 5 - Machine learning Pipelines (ML-OPS)16 lectures • 1hr 25min

Module 6 - Advanced Model Deployment Strategy13 lectures • 2hr 15min

Module 7 - MLOps (Machine Learning Operations)8 lectures • 51min

Module 8: Exploring Generative AI with Azure ML Studio15 lectures • 2hr 53min

Requirements

Description

Who this course is for: