Azure Machine Learning Hands-on Course

This Azure Machine Learning hands-on course is designed for beginners who want to develop strong practical skills in building real-world machine learning systems using Microsoft Azure. The course emphasizes applied learning, guiding students through a complete end-to-end workflow starting from account setup and environment configuration all the way to data engineering, model development, and pipeline automation.

Unlike purely theoretical courses, this program focuses heavily on practical scenarios using real datasets, helping learners understand how machine learning is implemented in cloud environments used in industry.

1.1 Getting Started with Azure Machine Learning Environment

This section introduces the foundational steps required to begin working with Azure Machine Learning. Learners are guided through account creation, workspace setup, and understanding how Azure structures machine learning projects within the cloud ecosystem.

It also builds awareness of how cloud-based ML environments differ from local development setups and why Azure is widely used in enterprise AI solutions.

1.1.1 Creating Azure for Students Account

This part explains how to create an Azure for Students account and activate free cloud credits. Learners understand how this account provides access to Azure Machine Learning services, compute resources, and storage needed for building ML projects.

1.1.2 Setting Up Azure Machine Learning Workspace

Here learners create and configure an Azure Machine Learning workspace, which acts as the central hub for organizing datasets, experiments, compute resources, models, and deployment workflows.

They also learn how workspace structure supports collaboration between data scientists and machine learning engineers in real production systems.

1.2 Compute Instances and Cloud Environment Setup

This section focuses on building and configuring cloud compute environments inside Azure Machine Learning. Learners understand how compute instances and compute clusters are used to train models efficiently at scale.

It also introduces the concept of scalable computing resources and how cloud infrastructure supports heavy machine learning workloads without requiring local hardware.

1.2.1 Understanding Compute Instances

This topic explains compute instances as virtual machines in the cloud that provide ready-to-use environments for running Jupyter notebooks, training models, and executing experiments.

Learners understand how compute resources can be started, stopped, and managed based on workload requirements.

1.2.2 Preparing Cloud Environment for Experiments

Here learners explore how to configure environments by installing required libraries, setting dependencies, and ensuring reproducibility of experiments across different runs.

This step is essential for maintaining consistency in machine learning workflows.

1.3 Data Preparation and Feature Engineering

This section introduces one of the most critical parts of machine learning: data preparation. Learners work with real datasets and learn how to transform raw data into a clean, structured format suitable for modeling.

The focus is on real-world data challenges such as missing values, inconsistent formats, and noisy data.

1.3.1 Importing and Exploring Data from SQL Server

This part explains how to connect Azure Machine Learning to SQL Server databases and import structured datasets. Learners also explore how to analyze dataset structure, distributions, and relationships between variables before modeling.

1.3.2 Data Cleaning and Handling Missing Values

Here learners study how to clean datasets by identifying missing values, filling or removing incomplete records, and ensuring data quality before training machine learning models.

This step is essential for improving model accuracy and reliability.

1.3.3 Outlier Detection and Data Clipping

This topic focuses on detecting extreme values that can negatively affect model performance. Learners apply clipping techniques to reduce the impact of outliers and stabilize model training.

1.4 Feature Selection and Data Optimization

This section explains how selecting the right features improves model performance, reduces complexity, and prevents overfitting. Learners explore feature engineering techniques commonly used in production systems.

1.4.1 Filter-Based Feature Selection

This part explains statistical methods used to evaluate feature importance and select the most relevant variables for model training.

1.4.2 Reducing Irrelevant Features

Here learners understand how removing unnecessary or redundant features improves training speed, model accuracy, and interpretability.

1.5 Building Regression Models in Azure ML

This section focuses on predictive modeling using regression techniques. Learners build real-world models that predict continuous values based on historical data.

They also learn how machine learning models learn relationships between input features and target variables.

1.5.1 Linear Regression for Insurance Prediction

This topic demonstrates how linear regression is used to predict insurance-related outcomes based on customer or policy data.

Learners understand how model parameters are trained and how predictions are generated.

1.5.2 Working with Categorical Features

Here learners explore how categorical variables are converted into numerical formats using encoding techniques so they can be used in regression models.

1.6 Building Azure Machine Learning Pipelines

This section introduces automated machine learning workflows using Azure ML pipelines. Learners understand how pipelines connect multiple steps into a single structured workflow.

It also demonstrates how pipelines improve reproducibility, scalability, and efficiency in real-world machine learning projects.

1.6.1 Creating Machine Learning Pipelines

This part explains how to design pipelines that integrate data preparation, feature engineering, model training, and evaluation into a unified workflow.

1.6.2 Automating Machine Learning Workflows

Here learners learn how automation reduces manual effort, ensures consistency across runs, and enables production-level machine learning systems.

1.7 Final Skills and Learning Outcomes

By the end of this course, learners will be able to confidently design and implement end-to-end machine learning workflows using Azure Machine Learning.

They will have hands-on experience with data engineering, regression modeling, feature selection, and pipeline automation, preparing them for real-world AI and data science roles in cloud environments.