The complete Azure Machine learning course – 2025 Edition | Udemy

Machine learning is revolutionizing industries by enabling data-driven decision-making and automation. However, implementing machine learning models can be complex, requiring infrastructure setup, data processing, and model deployment. Microsoft Azure Machine Learning Studio simplifies this process by providing a cloud-based platform to build, train, and deploy machine learning models efficiently. This course is designed to help learners master Azure ML Studio through a structured, hands-on approach.

This course covers the entire machine learning lifecycle, from understanding key concepts to deploying models in production environments. Learners will explore:

• Types of Machine Learning – Supervised, unsupervised, and reinforcement learning.
• Real-world applications in healthcare, finance, cybersecurity, and retail.
• Challenges in Machine Learning – Overfitting, data quality, interpretability, and scalability.

Hands-on with Azure ML Studio

Through practical demonstrations, learners will:

• Navigate the Azure Machine Learning Studio interface and set up a workspace.
• Manage datasets, experiments, and models in a cloud-based environment.
• Preprocess data – Handle missing values, perform feature engineering, and split datasets for training.
• Use data transformation techniques – Standardization, normalization, one-hot encoding, and PCA.

Building & Training Machine Learning Models

Learners will explore different machine learning algorithms and techniques, including:

• Regression, classification, and clustering models in Azure ML Studio.
• Feature selection and hyperparameter tuning for better model performance.
• AutoML (Automated Machine Learning) for optimizing models with minimal effort.
• Ensemble learning methods such as Random Forests, Gradient Boosting, and Neural Networks.

Model Deployment & Optimization

Once models are trained, learners will dive into model deployment strategies:

• Real-time inference vs. batch inference using Azure Kubernetes Service (AKS) and Azure Functions.
• Security best practices – Role-Based Access Control (RBAC), compliance, and encryption.
• Monitoring model drift – Implementing tracking tools to detect performance degradation over time.

Automating Machine Learning Workflows

This course includes Azure ML Pipelines to automate machine learning processes:

• Building end-to-end pipelines – Automate data ingestion, model training, and evaluation.
• Using custom Python scripts in ML pipelines.
• Monitoring and managing pipeline execution for scalability and efficiency.

MLOps & CI/CD for Machine Learning

Learners will gain practical knowledge of MLOps and CI/CD for ML models using:

• Azure DevOps & GitHub Actions for model versioning and retraining automation.
• CI/CD pipelines for seamless ML model updates.
• Techniques for model lifecycle management – Deployment, monitoring, and rollback strategies.

Exploring Generative AI with Azure ML

This course also introduces Generative AI:

• Working with Azure OpenAI Services – GPT, DALL·E, and Codex.
• Fine-tuning AI models for domain-specific applications.
• Ethical AI considerations – Bias detection, explainability, and responsible AI practices.
• Microsoft Certified: Azure Data Scientist Associate –  DP-100