The Complete Azure Machine Learning Studio Course
Learn to build, train, and deploy predictive analytics solutions using Azure Machine Learning Studio. Master key machine learning concepts, create end-to-end ML pipelines, and gain hands-on experience developing models with real-world applications.
Last update: May 2025
Audio Language: English
Subtitles/Captions: Yes
Type of Training: Online self-paced
Focus: AI Coding Courses, Programming Courses, Generative AI Courses, Azure Courses, Cloud Computing Courses, Networking Courses
Caption Language: English
What You’ll Learn
This Complete Azure Machine Learning Studio Course will teach you:
- Azure Machine Learning Studio:
Build, train, and deploy models using Microsoft’s cloud-based platform. - Core machine learning concepts:
Understand supervised, unsupervised, and reinforcement learning types. - Model development:
Create, train, and evaluate models using visual tools and Python code. - Data preparation:
Load, clean, and transform datasets for better analysis and performance.
- Automated ML:
Use AutoML and hyperparameter tuning for optimized training. - Model deployment:
Deploy models as web services for real-time or batch inference. - MLOps practices:
Implement continuous delivery, monitoring, and model versioning. - Distributed training:
Scale workloads using Azure Compute Clusters and parallel processing.
Included in the Azure Machine Learning Studio
16 Hours of On-Demand Video
6 Downloadable Resources
Access on Mobile
Certificate of Completion
Suitable for the Following Careers
Data Scientist
Machine Learning Engineer
Cloud AI Specialist
Azure Developer
AI/ML Analyst
Data Analyst
Course Content
PREVIEW COURSEDescription of Training
Description of Training
Preview
Module 1: Introduction to Machine Learning and Azure
Introduction to Machine Learning and Azure
Preview
1.1 What is Machine Learning
Definition and overview of machine learning (ML)
Preview
Types of machine learning Supervised, Unsupervised, Reinforcement Learning
Preview
Key concepts Training data, features, labels, models, predictions
Preview
Real-world applications of ML in industries such as healthcare, finance, and retail
Preview
Challenges in machine learning Overfitting, underfitting, data quality, and interpretability
Preview
1.2 Overview of Azure Machine Learning Studio
1.3 Key Features and Benefits
1.4 Setting Up and Navigating Your Azure Account
1.5 Getting Started with Azure ML Studio
Module 2: Data Basics and Preprocessing
2.1 Loading and Exploring Datasets in Azure ML Studio
2.2 Data Cleaning Techniques
2.3 Splitting and Normalizing Datasets
2.4 Feature Engineering Basics
Module 3: Building Machine Learning Models
3.1 Choosing Algorithms for Different Problems
3.2 Building Models Using Azure ML Studio
3.3 Advanced Model Building Techniques
Module 4: Model Evaluation and Optimization
4.1 Model Evaluation Metrics and techniques
4.2 Advanced Optimization Techniques
4.3 Model Monitoring and Updating
Module 5: Machine Learning Pipelines
5.1 Creating Reusable Pipelines for ML Workflows
5.2️ Integrating Custom Python Scripts in Pipelines
5.3 Advanced Features in Azure ML Pipelines
Module 6: Advanced Model Training and Deployment
6.1 Distributed Training Using Azure Compute Clusters
6.2 Model Deployment Options_ Real-Time and Batch Inference
6.3 Managing Secure Endpoints and Monitoring Models
6.4 Advanced Deployment Strategies
Module 7: MLOps (Machine Learning Operations)
7.1 Introduction to MLOps
7.2 CI-CD for Machine Learning Models
Module 8: Exploring Generative AI with Azure ML Studio
8.1 Understanding Generative AI
8.2 Fine-Tuning Generative AI Models
8.3 Ethical Considerations in Generative AI
Audio Version of Training
OPEN FULL CURRICULUM
Bonus Lecture - Whats Next?Requirements
A Microsoft Azure account
Basic understanding of data handling
Stable internet connection
Description of The Complete Azure Machine Learning Studio Course
Explore the full potential of Azure Machine Learning Studio as you develop, configure, and deploy real machine learning solutions. This course walks you through the process of turning datasets into predictive analytics solutions using Azure’s integrated tools and cloud-based capabilities.
You’ll work with data scientists’ favorite tools—including Jupyter notebooks, AutoML modules, and scalable training environments—to build and manage production-grade models. As you progress, you’ll test algorithms, manage experiment logs, and automate deployment using Azure resources and machine learning pipelines.
- Create regression, classification, and clustering models with Azure ML Studio
- Test model performance, analyze datasets, and log results
- Deploy predictive analytics models as secure web services
- Build and reuse machine learning pipelines for continuous development
- Adjust parameters and configure settings for optimal training
- Monitor model performance and retrain using MLOps best practices
By the end of this course, you’ll be ready to analyze datasets, configure model parameters, and fine-tune algorithm settings to deliver impactful AI solutions using Azure Machine Learning. You'll understand how to log results, manage modules, and work with files, values, and detailed experiment outputs, empowering developers to build scalable, cloud-based machine learning solutions with confidence.
Who Is This Course For
This course is ideal for beginners entering machine learning or Azure, data professionals wanting to deploy predictive analytics solutions, and cloud engineers looking to add AI capabilities to their skill set. It's also perfect for those preparing for Azure certifications.
Course Instructor
Anand Nednur
Anand Rao is a senior technical instructor and cloud consultant with 15 years of experience working with large enterprises. He is proficient in cloud platforms (Azure, AWS, GCP), IAM, security, and automation with PowerShell and Python. Anand excels in developing course content and helping engineers secure certifications.
He has delivered training across India and internationally in the USA, Bahrain, Kenya, and UAE. Anand is also a Microsoft Certified Trainer and holds certifications including CompTIA Security+, Scrum Certified Master, ITIL V3, Certified Network Defender, Certified Ethical Hacker, MS Active Directory, MS Azure Administration, MS Azure Architecture, AWS Certified Solutions Architect, AWS Certified SysOps Administrator, Google Cloud Platform-Cloud Architect, and Certified Cloud Security Knowledge.
Anand's comprehensive knowledge and dedication to teaching have made him a trusted resource in the field of cybersecurity and cloud computing.
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Testimonials
Daniel I
The Complete Azure Machine Learning Course is a masterpiece. The instructor's expertise and passion shine through. I've learned so much and appreciate the support provided
Ramona A.
Among the machine learning classes I've taken, this one is particularly noteworthy. The instructor offers first-rate assistance, and the material is thorough. Strongly advised
Crystal S.
This course is revolutionary! Clear and succinct instruction is the instructor's style. I can now use Azure Machine Learning in practical applications because I have firsthand expertise with it. Five stars is insufficient!
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