Azure Machine Learning Designer Course

This Azure Machine Learning Designer course is designed for beginners who want to build, train, and evaluate machine learning models using a fully visual, no-code approach inside Microsoft Azure. The course focuses on simplifying the machine learning workflow by using Azure ML Designer’s drag-and-drop interface instead of traditional programming.

Learners are guided through the complete lifecycle of a machine learning project, starting from environment setup and data preparation all the way to model building, evaluation, and interpretation.

1.1 Getting Started with Azure Machine Learning Designer

This section introduces learners to Azure Machine Learning Designer and explains how it fits into the broader Azure Machine Learning ecosystem. It focuses on how users can create machine learning pipelines visually without writing code, making it ideal for beginners and non-programmers.

1.1.1 Creating Azure Account and Workspace Setup

This part explains how to create an Azure student account and set up an Azure Machine Learning workspace, which is required to access Designer tools and manage experiments.

1.1.2 Introduction to Azure ML Designer Interface

Here learners explore the Azure ML Designer interface, including how to navigate the workspace, access datasets, and build visual pipelines using drag-and-drop components.

1.2 Compute Setup and Environment Configuration

This section focuses on setting up compute resources required to run machine learning workflows inside Azure ML Designer.

Learners understand how cloud compute works and how to configure it for running experiments efficiently.

1.2.1 Creating Compute Instances

This topic explains how to create compute instances in Azure ML, which are used to execute workflows and run experiments in the cloud environment.

1.2.2 Managing Cloud Resources for Designer Workflows

Here learners understand how to manage compute resources to ensure smooth execution of machine learning pipelines while controlling cost and performance.

1.3 Data Preparation and Transformation

This section introduces data preprocessing techniques using Azure ML Designer tools. It focuses on preparing raw data so it can be used effectively in machine learning models.

1.3.1 Data Cleaning and Summarization

This part explains how to clean datasets, handle missing values, and generate summary statistics using visual tools inside Azure ML Designer.

1.3.2 Data Normalization and Feature Preparation

Here learners explore how to normalize and transform data so that machine learning models can learn more effectively from structured inputs.

1.4 Building Machine Learning Experiments

This section focuses on creating machine learning experiments using Azure ML Designer’s visual pipeline system.

Learners build complete workflows from data input to model training using drag-and-drop modules.

1.4.1 Creating Visual Machine Learning Pipelines

This topic explains how to design and build machine learning pipelines using connected modules such as data input, transformation, and model training blocks.

1.4.2 Running and Managing Experiments

Here learners understand how to execute experiments, monitor their progress, and manage multiple training runs inside Azure ML.

1.5 Model Training and Evaluation

This section introduces how machine learning models are trained and evaluated inside Azure ML Designer without writing code.

1.5.1 Training Models Using Drag-and-Drop Interface

This part explains how to select algorithms, train models, and configure training settings using a visual interface.

1.5.2 Evaluating Model Performance

Here learners learn how to measure model accuracy and performance using evaluation metrics provided by Azure ML Designer.

1.6 Model Interpretation and Explainability

This section focuses on understanding how machine learning models make predictions and how to interpret their results.

1.6.1 Understanding Model Predictions

This topic explains how predictions are generated and how different input features influence model output.

1.6.2 Explainable AI in Azure ML Designer

Here learners explore explainability tools that help interpret model behavior and improve transparency in machine learning systems.

1.7 Final Skills and Learning Outcomes

By the end of this course, learners will be able to confidently build, train, evaluate, and interpret machine learning models using Azure Machine Learning Designer without writing code.

They will also understand how to design complete end-to-end machine learning workflows using a visual, beginner-friendly approach suitable for real-world applications.