Microsoft Azure Machine Learning Fundamentals (Video)

Description

Learn and implement Machine Learning Fundamentals tools.

Azure Machine Learning is a fully managed cloud service designed to accelerate and streamline the entire machine learning lifecycle. It provides data scientists and ML engineers with powerful tools to build, train, deploy, and manage machine learning models at scaleall within a secure, enterprise-ready environment. The platform supports popular frameworks like PyTorch, TensorFlow, and scikit-learn, and includes robust MLOps capabilities for monitoring, retraining, and governance. Its collaborative workspace, automated workflows, and integration with the broader Azure ecosystem make it ideal for teams seeking efficient, repeatable, and production-grade machine learning solutions.

This video course combines slides that provide an overview of key concepts with hands on demonstrations and lab in Azure. This approach gives learners foundational knowledge before they begin implementing models in Azure. The course explains what machine learning actually is and why its such an important part of building modern applications in the cloud. Youll explore core concepts like how models learn from data, the different types of machine learning, and how cloud platforms make AI more accessible and scalable. By the end of these lessons, you will have a clear mental model of how machine learning works and where it fits into real-world solutions.

You'll have access to hands-on demos that show how to build, train, and manage machine learning models using Azure. Youll walk through practical workflows like setting up environments, training models, tuning performance, and deploying solutions that can be monitored and improved over time. After completing the course, you'll have practical skills you can immediately apply, plus the confidence to use Azures machine learning tools in real projects or professional environments.

Skill Level:

Beginner to Intermediate

Learn How To:

• Understand machine learning fundamentals and how models learn from data to solve real-world business problems.
• Build, train, and evaluate machine learning models using Azure Machine Learning and its integrated tools.
• Optimize model performance through tuning, automated workflows, and best practices for scalable ML development.
• Deploy, monitor, and manage production-ready models with Azure MLOps capabilities, governance, and lifecycle management.

Course Requirements:

Pre-requisites:

• Basic understanding of programming concepts, such as variables, functions, and conditional logic
• Familiarity with cloud concepts, including what cloud services are and how they are used
• Comfort working with data in simple formats like tables, CSV files, or spreadsheets
• General awareness of how web or software applications are built and deployed

Who Should Take This Course:

Job Titles: This audience commonly includes software developers, cloud engineers, data analysts, technical leads, and early-career data scientists.

About Pearson Video Training:   

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training at  http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents

Introduction

Lesson 1: Machine Learning Introduction

1.1 Discover machine learning fundamentals

1.2 Explore machine learning pipelines

1.3 Use a data-centric approach to machine learning

1.4 Apply the best algorithms to your problems

1.5 Demo: Create a plan for your machine learning implementation

1.6 Lab: Create a plan for your machine learning implementation

Lesson 2: Azure Machine Learning Introduction

2.1 Discover Azure Machine Learning Studio

2.2 Create an Azure Machine Learning workspace

2.3 Support the full machine learning lifecycle

2.4 Use the Azure Machine Learning API

2.5 Demo: Explore Azure Machine Learning designer and API

2.6 Lab: Use Azure Machine Learning designer and API

Lesson 3: Data, Datasets, and Model Training

3.1 Use of data in machine learning

3.2 Understand training vs. test data

3.3 Discover common model types

3.4 Explore training workflows

3.5 Demo: Import and use data in Azure Machine Learning

3.6 Lab: Import and use data in Azure Machine Learning

Lesson 4: Automated Machine Learning and Model Evaluation

4.1 Understand when and how to use automated machine learning

4.2 Learn key evaluation metrics

4.3 Explore model improvement and advanced optimization content

4.4 Demo: Try automated machine learning and evaluation

4.5 Lab: Try automated machine learning and evaluation

Lesson 5: Deploying and Consuming Machine Learning Models

5.1 Deploy a model with Azure Machine Learning

5.2 Use real-time endpoints

5.3 Use batch predictions

5.4 How applications consume machine learning models

5.5 Demo: Deploy a machine learning model with Azure Machine Learning

5.6 Lab: Deploy a machine learning model with Azure Machine Learning

Lesson 6: Improving, Monitoring, and Responsible AI

6.1 Detect model drift

6.2 Monitor model performance throughout the MLOps pipeline

6.3 Retrain models

6.4 Understand interpretability, explainability, or fairness

Summary

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