Coursera – Microsoft AI & ML Engineering Professional Certificate 2025-5

Coursera – Microsoft AI & ML Engineering Professional Certificate 2025-5 Downloadly IRSpace

Aug 16, 2025 - 07:10 Updated: Aug 17, 2025 - 19:47

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Coursera – Microsoft AI & ML Engineering Professional Certificate 2025-5

Microsoft AI & ML Engineering Professional Certificate, Prepare for a Career in AI & ML Engineering. Build, deploy, and innovate with advanced techniques and real-world projects. Intermediate programming knowledge of Python required. This comprehensive program is designed to prepare you for the dynamic field of artificial intelligence and machine learning. Across five courses, you gain a deep understanding of AI & ML fundamentals, practical skills, and hands-on experience. Starting with the design of scalable AI & ML infrastructure, you learn to build robust environments. You then explore core algorithms and techniques. The program also delves into AI agent development, teaching you how to create intelligent systems capable of autonomous troubleshooting using natural language processing (NLP) and decision-making strategies. A key focus is on leveraging cloud-based AI & ML services, specifically through Microsoft Azure, where you manage end-to-end workflows. The program concludes with advanced concepts, ethical considerations, and a capstone project. Upon completion, you will have the expertise to design, deploy, and optimize AI & ML solutions, making you a valuable asset in the tech industry. This program is ideal for those seeking to master AI & ML techniques, build scalable solutions, and apply your knowledge to real-world problems. To be successful, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended. You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware.

What you’ll learn

Design and Implement AI & ML Infrastructure: Develop environments, including data pipelines, model development frameworks, and deployment platforms.
Master AI & ML Algorithms and Techniques: Apply supervised, unsupervised, reinforcement learning, and deep learning methods to solve challenges.
Develop Intelligent Troubleshooting Agents: Create AI-powered agents capable of diagnosing and resolving issues autonomously.
Leverage Microsoft Azure for AI & ML Workflows: Set up, manage, and optimize the entire AI & ML lifecycle using Azure.