Microsoft Cloud Workshop: Machine Learning Course Overview

Target Audience for Microsoft Cloud Workshop: Machine Learning

1. The Microsoft Cloud Workshop: Machine Learning course offers hands-on experience in creating advanced machine learning models, catering to IT professionals and data scientists.

   
   

2. Target audience for the course includes:

   
   

• Data Scientists
• Machine Learning Engineers
• AI/ML Researchers
• Data Analysts
• IT Professionals with an interest in machine learning
• Cloud Solutions Architects
• Software Engineers looking to specialize in machine learning
• DevOps Engineers focusing on machine learning pipelines
• Technical Team Leaders managing machine learning projects
• Product Managers overseeing ML-based products
• Consultants providing machine learning solutions
• Technical Sales Professionals involved in AI/ML solutions

Learning Objectives - What you will Learn in this Microsoft Cloud Workshop: Machine Learning?

Introduction to Course Learning Outcomes and Concepts Covered

This course empowers students with hands-on experience in designing and deploying machine learning solutions using Microsoft Cloud technologies, with a focus on real-world applications and best practices.

Learning Objectives and Outcomes

• Understand the customer case study to align machine learning solutions with business requirements.
• Design a proof of concept for a machine learning solution that addresses a real-world problem.
• Develop presentation skills to effectively communicate the designed solution to stakeholders.
• Acquire practical skills in creating forecast models using Azure's automated machine learning capabilities.
• Learn to interpret machine learning models and their predictions using model explainability tools.
• Gain experience in building and training deep learning models, specifically Recurrent Neural Networks (RNNs), for time series analysis.
• Master the process of registering and managing machine learning models within the Azure ecosystem.
• Implement techniques for real-time scoring of streaming data using trained forecast models.
• Construct deep learning models for text classification, enhancing the ability to process and analyze textual data.
• Explore best practices for deploying and operationalizing machine learning models in a cloud environment.