Overview

The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. Through practical demonstrations and real-world scenarios, the course ensures learners are prepared to build scalable AI solutions in Azure. The specialization is divided into four key courses, covering the domain requirements for the DP-100: Designing and Implementing a Data Science Solution on Azure exam: The detail of the Courses is provided below Course 1: Azure ML: Designing and Preparing Machine Learning Solutions Course 2: Azure ML: Explore & Configure the Machine Learning Workspace Course 3: Azure ML: Deploying, Managing, and Experimenting with Models Course 4:Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. Some of the important benefits of achieving these certifications include: Industry Recognition: Validates expertise in Azure Machine Learning. Career Growth: Enhances job prospects in AI, ML, and cloud-based data science. Higher Earning Potential: Opens doors to high-paying roles in data science and AI.

Syllabus

Azure Machine Learning Workspace: Essentials

This module provides a comprehensive foundation in data science and machine learning, equipping learners with the essential knowledge required for the DP-100 certification. Participants will explore key concepts such as data science processes, machine learning fundamentals, and statistical modeling, ensuring a strong grasp of core principles. The module covers different types of machine learning, common terminologies, and data aspects relevant to model development. Learners will also gain insights into best practices for selecting and managing machine learning solutions, including preprocessing techniques and evaluation methodologies. By the end of this module, participants will develop a structured understanding of data science workflows, enabling them to apply these skills effectively in real-world scenarios and certification preparation

Explore and Configure the Azure ML Workspace

This module provides a comprehensive understanding of compute and data management within Azure Machine Learning, equipping learners with the skills to efficiently configure and optimize ML workflows. Participants will explore key concepts such as creating and managing compute instances, clusters, and attached computes within the Azure ML workspace. The module covers CPU vs. GPU selection for different workloads, datastore and data asset management, and leveraging Uniform Resource Identifiers (URIs) for resource identification. Learners will gain expertise in configuring environments, integrating Synapse Spark pools, and training machine learning models with Azure ML. Additionally, the module includes valuable exam tips to ensure learners are well-prepared for certification. By the end of this module, participants will be equipped with practical knowledge to manage compute and data resources effectively within Azure ML for scalable AI solutions