Description

Course Overview

This course is designed to prepare you for the DP-100 Microsoft Azure Data Scientist Certification Exam. It covers all the critical topics required to design and implement machine learning solutions using Azure Machine Learning. Through hands-on projects and in-depth lessons, you'll gain practical experience and the confidence to tackle real-world challenges and ace the certification exam.

Introduction

This section introduces the course, outlines its objectives, and explains the exam requirements. It sets the stage by familiarizing students with what they’ll achieve and the skills they’ll gain.

Create an Azure Machine Learning Workspace

Learn how to create an Azure ML workspace, manage its settings, and navigate the Azure portal and ML Studio. This foundational knowledge ensures you’re ready to work in Azure’s machine-learning environment.

Azure Learning Workspace

Explore data storage and dataset management within Azure ML. Learn how to create and manage datasets, preparing data for experiments and machine-learning pipelines.

Manage Experiment Compute Context

Understand compute instances and clusters for running experiments. This section explains setting up and managing compute targets to optimize resource utilization and execution speed.

Using Azure Machine Learning

Create your first machine-learning pipeline and submit it for execution. Dive into custom coding, error handling, and exploring Azure ML Designer's modules to build robust pipelines.

Azure Machine Learning Experience

Get started with Azure SDK, set up your workspace programmatically, and create simple Python programs. Learn how Azure’s SDK streamlines ML tasks.

Run Training in an Azure Machine Learning Environment

Use the SDK to train models, submit experiments, and create complex pipelines. This section focuses on hands-on training and automation techniques for efficient workflows.

Automate ML to Create Optimal Models

Master Azure AutoML to automate model selection, tuning, and deployment. Learn how to use AutoML with SDK to achieve optimal results with minimal effort.

Use Hyperdrive to Tune Hyperparameters

Explore Hyperdrive, Azure’s hyperparameter tuning tool. Learn to register trained models, manage production compute targets, and optimize model performance efficiently.

Deploy Model as a Service

Deploy models for real-time inference or batch processing. Gain expertise in creating endpoints, deploying SDK-based models, and publishing pipelines for large-scale tasks.

Conclusion

Wrap up the course with a summary of the key learnings and discuss the potential next steps in your Azure ML journey, including certification or advanced real-world projects.

This course equips you with the skills to use Azure ML effectively for building, training, and deploying machine-learning models. Whether you’re a beginner or an experienced data professional, the hands-on projects and in-depth lessons will ensure you’re ready to tackle ML challenges with Azure's robust toolkit.

Who this course is for:

Aspiring data scientists and machine-learning engineers. Professionals looking to implement machine learning in production environments. Students preparing for Azure ML certifications. Developers transitioning to machine learning workflows using Azure tools.

What you'll learn

• Set up and navigate Azure Machine Learning workspaces.
• Build, train, and deploy machine learning models using Azure ML Designer and SDK.
• Automate ML workflows using AutoML and Hyperdrive.
• Deploy real-time and batch inference pipelines.
• Optimize hyperparameters and manage production-scale ML services.

Requirements

• Basic understanding of machine learning concepts. Familiarity with Python programming. Access to an Azure account for practical exercises.