Azure Machine Learning: No-Code AI Fundamentals — PickAClass
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★ 4.5 (4) ⏱ 3h 2m 📚 28 lessons

Azure Machine Learning: No-Code AI Fundamentals

Build and deploy predictive models using automated machine learning and visual tools without writing any code.

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About this course

Machine learning is the engine behind modern artificial intelligence, but you do not need to be a programmer to harness its power. This course guides you through the process of creating sophisticated predictive models using automated and visual tools, taking you from fundamental concepts to fully deployed services. You will learn how to: - Understand core machine learning concepts and the data science lifecycle. - Configure automated machine learning to identify the best models for your data. - Build custom workflows using visual drag-and-drop tools. - Deploy and test predictive models as accessible web services. - Apply responsible AI principles to ensure fairness and transparency in your projects. - Explore foundational MLOps concepts for basic model monitoring and management. The curriculum begins with essential terminology and basic definitions before moving into practical applications within the cloud environment. You will read through clear explanations and structured exercises designed to build your confidence in handling data and generating insights. This course is designed for absolute beginners with no prior coding or data science experience. Start your journey into the world of cloud-based artificial intelligence today.

Course contents

6 modules · 28 lessons · 3h 2m

1 Introduction to AI and Azure ML
Understand the basics of ML and set up the cloud environment. 6 lessons · 38 min
  1. What is Machine Learning?
    Machine Learning is the process of teaching computers to learn from data without explicit programming.    6 min
  2. Key Terminology: Features, Labels, and Models
    Understanding the basic building blocks of a dataset: what we use to predict and what we want to predict.    7 min
  3. The Data Science Lifecycle
    The iterative process of defining a problem, preparing data, training, and deploying a model.    6 min
  4. Azure Machine Learning Workspace
    The Workspace is a centralized place in the cloud to manage all your ML assets and activities.    6 min
  5. Navigating Azure ML Studio
    A tour of the web-based portal for all no-code and code-based ML tasks.    6 min
  6. Compute Resources for No-Code ML
    Setting up the virtual hardware (Compute Instances and Clusters) needed to run your experiments.    7 min
2 Preparing Data for Learning
Learn how to manage and clean data within Azure ML. 4 lessons · 25 min
  1. Understanding Data Assets
    Data assets are references to data sources that make it easy to version and reuse data.    6 min
  2. Importing Data from Local Files and Web
    How to upload your CSV or Excel files into the Azure ML cloud environment.    7 min
  3. Data Exploration and Visualization
    Using built-in tools to look at statistics and distributions of your data before training.    6 min
  4. Basic Data Cleaning Concepts
    Handling missing values and outliers to ensure high-quality model inputs.    6 min
3 Automated Machine Learning (AutoML)
Use AutoML to find the best model automatically. 5 lessons · 32 min
  1. The Power of Automated ML
    AutoML automates the time-consuming tasks of model selection and hyperparameter tuning.    6 min
  2. Configuring an AutoML Job
    Choosing the task type (Regression, Classification, or Forecasting) and target column.    7 min
  3. Selecting Primary Metrics
    Understanding how to tell Azure which 'score' matters most for your specific business goal.    6 min
  4. Analyzing AutoML Run Results
    Reviewing the leaderboard of models generated by Azure to see which performed best.    7 min
  5. Explanation of the Best Model
    Using the 'Explainability' feature to see which features influenced the winning model's predictions.    6 min
4 Visual Workflows with Designer
Build custom ML pipelines using the drag-and-drop interface. 5 lessons · 34 min
  1. Introduction to Azure ML Designer
    Designer provides a visual canvas to build, test, and deploy ML models using pre-built modules.    6 min
  2. Building a Training Pipeline
    Connecting modules for data input, splitting data, and training an algorithm.    8 min
  3. Data Transformation Modules
    Using visual components to normalize data, select columns, and clean missing values.    7 min
  4. Evaluating Model Performance
    Using the Evaluate Model module to visualize metrics like Accuracy or Mean Absolute Error.    7 min
  5. AutoML vs. Designer: When to Use Which?
    Choosing between full automation and manual visual control based on your project needs.    6 min
5 Deployment and Testing
Turn your model into a working web service. 4 lessons · 27 min
  1. Model Registration
    Saving your trained model in the registry to keep track of versions and metadata.    6 min
  2. Deploying to a Real-Time Endpoint
    Creating a web service that can provide predictions instantly via an API.    8 min
  3. Testing the Web Service
    Using the built-in 'Test' tab to send sample data and verify the model's response.    7 min
  4. Consuming the Model
    Understanding how external apps use the REST endpoint and API keys to get predictions.    6 min
6 Responsible AI and MLOps
Ensure your AI is ethical and managed professionally. 4 lessons · 26 min
  1. Principles of Responsible AI
    The six pillars: Fairness, Reliability, Privacy, Inclusiveness, Transparency, and Accountability.    7 min
  2. Fairness and Transparency in Azure
    Using the Responsible AI dashboard to detect bias and understand model behavior.    7 min
  3. Introduction to MLOps
    MLOps is the practice of applying DevOps principles to automate and simplify ML workflows.    6 min
  4. Basic Model Monitoring
    Tracking model performance over time to ensure it remains accurate as real-world data changes.    6 min
Final project

The 'Predictive Price' End-to-End Project

You will take a raw dataset of automobile prices, clean it, use AutoML to find the best algorithm, build a manual pipeline in Designer for comparison, and finally deploy the best version as a web service.

What you'll get

  • 📜 Certificate of completion
    Add it to your LinkedIn profile
  • 💬 Personal AI tutor
    Stuck on a lesson? Ask your built-in tutor anything, any time.
  • ♾️ Lifetime access
    Come back anytime, no expiry
  • 📱 Phone or computer
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  • 💸 14-day refund
    No questions asked
  • ⚡ Short & focused
    3h 2m of practical content

Certificate of completion

Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.

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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Azure Machine Learning: No-Code AI Fundamentals
Skills demonstrated
✓
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Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
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Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
Skill verification Verified Skill Path
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Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

Reviews (4)

Mária Horváthová SK Verified learner
★ 4 · November 1, 2026

Exceeded my expectations! The structure was logical, and the real-world scenarios really helped cement the learning. Great value.

Fatimahtul Zahra binti Abdullah MY Verified learner
★ 4 · November 1, 2026

Fantastic course. The examples used were spot on and really helped solidify the concepts. My understanding has improved dramatically.

Charlie Robinson AU
★ 5 · August 7, 2026

This course exceeded my expectations. The real-world applications discussed are incredibly useful. Great job!

Manon Lambert LU Verified learner
★ 5 · July 2, 2026

This course exceeded my expectations! The structure was logical, and the explanations were crystal clear. Gained so much knowledge.

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Can I get a refund? +

Yes — full refund within 14 days, no questions asked.

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Forever. Once you purchase, the course is yours to revisit anytime.

Will I get a certificate? +

Yes. On completion you'll receive a certificate you can add to your LinkedIn profile.

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