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.

Instructor: Microsoft

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.

    • Skills you'll gain

  •   Microsoft Azure
  •   Unsupervised Learning
  •   Artificial Intelligence
  •   Data Management
  •   Applied Machine Learning
  •   Prompt Engineering
  •   Cloud Infrastructure
  •   Infrastructure Architecture
  •   Generative AI Agents
  •   Application Deployment
  •   Deep Learning
  •   Generative AI

    • Professional Certificate - 5 course series

    This project allows you to experience firsthand the process of turning an idea into a fully functional AI & ML solution, preparing you for advanced roles in AI & ML engineering.

    By the end of this course, you will be able to: 1. Analyze, describe, and critically discuss the critical components of AI & ML infrastructure and their interrelationships. 2. Analyze, describe, and critically discuss efficient data pipelines for AI & ML workflows. 3. Analyze and evaluate model development frameworks for various AI & ML applications. 4. Prepare AI & ML models for deployment in production environments. To be successful in this course, 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.

    By the end of this course, you will be able to: 1. Implement, evaluate, and explain supervised, unsupervised, and reinforcement learning algorithms. 2. Apply feature selection and engineering techniques to improve model performance. 3. Describe deep learning models for complex AI tasks. 4. Assess the suitability of various AI & ML techniques for specific business problems. To be successful in this course, 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.

    By the end of this course, you will be able to: 1. Define, describe, and design the architecture of an intelligent troubleshooting agent. 2. Implement natural language processing techniques for user interaction. 3. Develop decision-making algorithms for problem diagnosis and resolution. 4. Optimize and evaluate the performance of AI-based troubleshooting agents. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure and core algorithms and techniques, including approaches using pretrained large-language models (LLMs). Familiarity with statistics is also recommended.

    By the end of this course, you will be able to: 1. Configure and manage Azure resources for AI & ML projects. 2. Implement end-to-end ML pipelines using Azure services. 3. Deploy and monitor ML models in Azure production environments. 4. Troubleshoot common issues in Azure AI & ML workflows. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, and the design and implementation of intelligent troubleshooting agents. Familiarity with statistics is also recommended.

    By the end of this course, you will be able to: 1. Implement advanced ML techniques such as ensemble methods and transfer learning. 2. Analyze ethical implications and develop strategies for responsible AI. 3. Design scalable AI & ML systems for high-performance scenarios. 4. Develop and present a comprehensive AI & ML solution addressing a real-world problem. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, the design and implementation of intelligent troubleshooting agents, and Microsoft Azure’s AI & ML services. Familiarity with statistics is also recommended.

    AI and Machine Learning Algorithms and Techniques

    Building Intelligent Troubleshooting Agents

    Microsoft Azure for AI and Machine Learning

    Advanced AI and Machine Learning Techniques and Capstone

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