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.

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

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 StudioThrough 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 ModelsLearners 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 & OptimizationOnce 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 WorkflowsThis 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 LearningLearners 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 MLThis 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.

Overview

Section 1: Introduction to Machine Learning and Azure
Lecture 1 Definition and

Overview

of machine learning (ML)
Lecture 2 Types of machine learning Supervised, Unsupervised, Reinforcement Learning.
Lecture 3 Key concepts Training data, features, labels, models, predictions
Lecture 4 Real-world applications of ML in industries such as healthcare, finance, and r
Lecture 5 Challenges in machine learning Overfitting, underfitting, data quality, and in
Lecture 6 Introduction to Azure ML Studio and its capabilities for building, training, a
Lecture 7

Overview

of the Azure Machine Learning workspace Datasets, experiments, models
Lecture 8 Key components Designer, Notebooks, Automated ML, and Model Management
Lecture 9 Key features Visual interface, AutoML, integration with Azure services (Data F
Lecture 10 Scalability and flexibility with Azure Compute and storage options
Lecture 11 Collaboration and sharing Team-based development and version control
Lecture 12 Benefits Faster experimentation, model deployment, and continuous learning
Lecture 13 Creating an Azure account
Lecture 14 Exploring Azure Cloud Interface and Services Part-1
Lecture 15 Exploring Azure Cloud Interface and Services Part-2
Lecture 16 Exploring Azure Cloud Interface and Services Part-3
Lecture 17 Creating Azure ML Studio
Lecture 18 Exploring key features and benefits of Azure ML Studio
Lecture 19

Overview

of resource management Workspaces, compute resources, and storage acc
Lecture 20 Connecting to data sources and Azure services.
Section 2: Data Basics and Preprocessing
Lecture 21 Importing datasets from various sources local files, Azure Blob Storage, SQL d
Lecture 22 Exploring dataset statistics and visualizing data distribution
Lecture 23 Understanding data types (numerical, categorical, text, image)
Lecture 24 DEMO Loading a dataset and exploring basic statistics in Azure ML Studio
Lecture 25 Identifying and handling missing data ( Null, Nan Values )
Lecture 26 Outlier detection and treatment strategies
Lecture 27 Removing duplicates and irrelevant issues
Lecture 28 Correcting data types and formatting issues
Lecture 29 DEMO - Cleaning a dataset by handling missing values and outliers in ML Studio
Lecture 30 Exploring ML Studio Designer and Setting up an Experiment
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.,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.


Homepage:

https://www.udemy.com/course/the-complete-azure-machine-learning-course-2025-edition/