Applied Generative AI Specialization (in collaboration with with Michigan Engineering Professional Education & Microsoft )

Applied Generative AI Specialization Bootcamp

In collaboration with Michigan Engineering Professional Education, University of Michigan Online & Microsoft

• Develop the expertise to design and build AI agents that can think, plan, and operate autonomously.
• Join interactive masterclasses led by industry experts
• 16 Week length program (8-10/week weekend classes)
• Ask us for the next cohort and schedule details!

The Applied Generative AI Specialization in collaboration with Michigan Engineering Professional Education, is a hands-on program designed to help professionals build practical, scalable, and responsible generative AI solutions. Ideal for IT and data professionals, product and program managers, consultants, and early-career technologists, the specialization focuses on applying GenAI to real business and technical challenges.

The curriculum spans Python fundamentals, AI literacy, generative AI models and architectures, LLM application development, agentic AI, and GenAI governance. Learners gain experience with advanced techniques such as prompt engineering, retrieval-augmented generation (RAG), and model fine-tuning, while applying best practices for transparency, fairness, security, and regulatory compliance.

Graduates earn recognized certificates from Michigan Engineering Professional Education and Microsoft Azure, validating their ability to design, deploy, and lead AI-driven transformation across organizations.

Key Features

• Course and material are in English
• in collaboration with Michigan Engineering Professional Education Online
• Intermediate to advanced level
• 16 Weeks program (5-6 hours/week weekend classes)
• 70+ hours of live classes led by industry experts
• 300+ hours of study time and practice recommended
• 1 year course access & session recordings
• Work with 12+ modern AI tools, including OpenAI, Stable Diffusion, Microsoft Copilot, and Streamlit
• Get a Microsoft course completion certificate hosted on the MS Learn portal
• Program completion certificate from Michigan Engineering Professional Education Online.
• Secure a Michigan Eng Pro-Ed digital badge

Engaging Learning Experience

• Peer Interaction
• Enjoy a true classroom-like environment by connecting with fellow learners and engaging with mentors in real time through Slack.
• Flexible Learning
• Never fall behind—access recorded sessions anytime to catch up and stay aligned with your cohort.
• Mentorship Sessions
• Receive expert support from mentors to resolve doubts, get project guidance, and enhance your learning journey.
• Dedicated Support
• Benefit from a Cohort Manager who provides personalized assistance and ensures you stay on track toward success.

About University of Michigan

The University of Michigan is a leading public research university in the United States, globally recognised for academic excellence, innovation, and leadership in science and engineering. Michigan Engineering Professional Education, part of the university’s College of Engineering, delivers industry-focused programs led by expert U.S.-based faculty, helping professionals and leaders apply cutting-edge research and engineering practices to real-world business and technology challenges.

What added value does University of Michigan contribute to the program?

The program curriculum is reviewed and approved by Michigan Engineering Professional Education, Please be aware that the live classes are not held by actual University faculty staff but by many experienced Industry experts. While the instructors themselves are not employees of the University of Michigan. Beyond content approval, Michigan Engineering Professional Education also oversees instructor evaluation, quality assurance, learner satisfaction, and overall program outcomes which gives the program quality legitimacy and a co-branded certificate of completion.

Learning Objective

• Build a solid programming foundation to write, run, and optimize Python code for AI and ML applications
• Develop a strong understanding of AI, machine learning, and generative AI concepts, including ethical considerations and real-world use cases
• Explore generative models such as variational autoencoders (VAEs), generative adversarial networks (GANs), and large language models (LLMs)
• Apply advanced prompt engineering, retrieval-augmented generation (RAG), and fine-tuning techniques for practical LLM applications
• Work with agentic frameworks to design and build intelligent AI agents using LangChain
• Implement best practices for AI transparency, fairness, security, and regulatory compliance
• Use Stable Diffusion and autoencoders to create high-quality images and expand AI’s creative potential
• Design, develop, and deploy responsible AI-driven solutions to address real business challenges
• Gain hands-on experience with tools such as Gemini, FAISS, Azure AI Studio, and Hugging Face

Skills Covered

• Prompt Engineering
• Agentic Frameworks
• AI Agents
• Retrieval-Augmented Generation (RAG)
• LangChain for Workflow Design
• Stable Diffusion & AI Image Generation
• LLM Fine-Tuning
• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)
• Attention Mechanisms
• Transformers
• LLM Benchmarking
• GenAI Application Development
• GenAI Governance
• Model Context Protocol (MCP)

Target Audience:

This program caters to working professionals from various industries and backgrounds, fostering a collaborative and engaging learning atmosphere. With Generative AI emerging as a strong career path for both beginners and experienced experts, the Applied Generative AI Specialization is well-suited for individuals who have fundamental programming knowledge and an analytical mindset, and are eager to enhance their skills in the latest Generative AI innovations, including:

• IT Professionals
• Data Analysts
• Business Analysts
• Data Scientists
• Software Developers
• Analytics Managers
• Data Engineers
• Product Managers
• Program Managers
• Tech Consultants

Prerequisites:

• Must be 18 years or older with a high school diploma (or equivalent)
• Should possess a basic grasp of programming concepts and mathematics
• Ideally have 2+ years of professional experience, though it’s not required

Learning Path

1. Python Refresher with AI (Optional)
2. AI Literacy
3. Advanced Generative AI - Models and Architecture
4. Advanced Generative AI - Building LLM Applications
5. Agentic AI Frameworks with Model Context and Tooling Protocols
6. Advanced Generative AI - Image Generation
7. Generative AI Governance
8. Capstone Project

Electives

1. Microsoft Azure AI Fundamentals: Generative AI
2. Build a Foundation to Extend Microsoft 365 Copilot
3. Masterclass

COURSE CONTENT DETAILS

Course 1: Python Refresher with AI

Python is the core programming language behind AI development, making this course an essential starting point for applied generative AI. By mastering Python fundamentals, you’ll build a strong technical foundation to progress into AI model training, deployment, and governance.

Learning Outcomes

• Write and run Python programs to address real-world challenges
• Use data types, operators, and control structures to create efficient code
• Apply functions, loops, and error handling for well-structured programs
• Manage files effectively for AI and machine learning applications
• Build core coding skills needed for AI model development

Topics Covered

• Introduction to Python Programming
• Python Data Types and Operators
• Conditional Statements and Loops
• Error and File Handling
• Python Functions

Course 2: AI Literacy

With a solid grounding in Python, the AI Literacy course connects coding fundamentals to real-world AI applications, enabling you to evaluate AI feasibility, refine AI-generated outputs, and integrate AI solutions into practical business and creative scenarios.

Learning Outcomes

• Distinguish between AI, machine learning, and deep learning concepts
• Examine major AI breakthroughs and their influence on industry innovation
• Understand how transformers and NLP power modern AI applications
• Apply generative AI techniques to practical, real-world use cases
• Explore the open-source generative AI ecosystem using the Hugging Face platform
• Assess ethical, security, and governance considerations in AI
• Use ChatGPT and other GPT models across a range of business applications

Topics Covered

• Overview of AI, ML, and DL
• Advanced AI Models, Transformers, and NLP
• Generative AI Fundamentals
• Introduction to the GenAI Open-Source Landscape
• AI Security, Ethics, and Future Trends
• Introduction to Prompt Engineering
• Hands-On Demos: GPTs and AI in Business

Course 3: Advanced Generative AI: Models and Architecture

After building a foundation in AI literacy, this course advances into models and architectures to deepen your technical understanding of how modern AI systems work. It explores generative models and transformer-based architectures, equipping you with a strong grasp of LLMs and attention mechanisms so you can design, fine-tune, and optimize AI models for real-world applications.

Learning Outcomes

• Examine the importance of generative AI and its influence across industries
• Distinguish between generative models such as VAEs, GANs, and transformers
• Analyze the architecture, training methods, and operational behavior of large language models (LLMs)
• Apply attention and multi-head attention techniques within transformer models
• Design scalable generative AI workflows using LangChain
• Implement fine-tuning approaches to tailor AI models for specialized use cases
• Evaluate the future impact and evolution of generative AI

Topics Covered

• Introduction to Generative AI Models
• How Generative AI Works
• Evaluating Model Quality in Generative AI
• Large Language Models (LLMs) and Future Considerations
• VAEs and GANs - Training Process
• Attention Mechanisms and Transformers: Image and Text Generation
• LangChain and Workflow Design

Course 4: Advanced Generative AI: Building LLM Applications

After mastering LLM models and architectures, this course shifts to real-world application. Building on your foundation, you’ll gain hands-on experience implementing LLM-powered solutions and learn how prompt engineering, LangChain frameworks, and fine-tuning techniques enable the creation of high-performance AI applications for practical use cases.

Learning Outcomes

• Design and implement optimized prompts to improve LLM performance
• Apply advanced prompt engineering methods such as zero-shot, few-shot, chain-of-thought, self-consistency, and tree-of-thought
• Use LangChain to build and optimize LLM-based applications
• Develop and deploy retrieval-augmented generation (RAG) systems for effective knowledge retrieval
• Fine-tune and adapt LLMs for domain-specific use cases
• Assess and monitor LLM performance across different applications

Topics Covered

• Advanced Prompt Engineering
• Advanced Prompting Techniques
• LangChain for LLM Applications
• Vector Stores, Retriever, and LangChain Agents
• RAG with LangChain
• LLM Fine-Tuning & Customization

Course 5: Agentic AI Frameworks with Model Context and Tooling Protocols

After establishing a foundation in core LLM use cases, this course moves into the next phase of generative AI with agentic systems and protocol-based integrations. You’ll gain hands-on experience designing intelligent agents, defining orchestration logic, and connecting them through secure, vendor-neutral frameworks using MCP, while working with tools such as LangGraph, AutoGen, and CrewAI to build flexible, tool-agnostic systems.

Learning Outcomes

• Understand the evolution of agentic AI and its core system design principles
• Explore perception layers, reasoning engines, and action execution workflows
• Build and configure AutoGen agents for multi-agent reasoning and collaboration
• Use LangGraph for task routing, orchestration logic, and automation pipelines
• Learn how MCP enables standardized, cross-platform tool integration
• Apply secure protocols, SDKs, and governance-aligned implementation practices
• Create structured agent workflows and multi-agent teams using CrewAI

Topics Covered

• Agentic AI Core Concepts: Properties and Real-World Applications
• LangGraph: Task Nodes, Parallelism, and Orchestration Flows
• CrewAI: Team-Based Agents, Toolchains, and Task Coordination
• Best Practices: Access Control, On-Prem Deployments, and Compliance Strategies
• LLM Agent Architecture: Perception, Reasoning, and Execution Modules
• AutoGen: Customizable Agent Systems and Collaborative Tasks
• MCP: Messaging Protocols, Interoperability, and Secure SDK Frameworks

Course 6: Advanced Generative AI: Image Generation

After creating autonomous AI agents, the focus shifts to enhancing their ability to produce high-quality images. By learning image generation techniques, agents can generate, refine, and optimize visual content, extending the power of AI-driven automation.

Learning Outcomes

• Explain the fundamentals of Stable Diffusion, denoising methods, and autoencoders for image generation
• Use Stable Diffusion models to create high-quality images from text prompts
• Implement shared embedding spaces to strengthen multimodal AI capabilities
• Design custom AI-generated images for a variety of practical applications

Topics Covered

• Stable Diffusion and Denoising
• Autoencoders and Contrastive Learning
• Shared Embedding Spaces for Image Generation
• Practical Applications

Course 7: Generative AI Governance

As AI-generated content grows increasingly sophisticated, it is vital to develop AI systems that are ethical, secure, and transparent. This course expands on existing technical skills by exploring governance issues, regulatory requirements, and best practices for the responsible use of generative AI. Gaining a solid understanding of AI governance helps reduce risks, preserve user trust, and ensure compliance in sectors utilizing AI-generated visuals and multimodal models.

Learning Outcomes

• Recognize the importance of governance in generative AI and its role in promoting ethical AI development.
• Identify major challenges in AI systems, including bias, misinformation, privacy, and security risks.
• Implement ethical frameworks and governance principles to ensure fairness, transparency, and accountability.
• Evaluate AI governance structures and adopt best practices to mitigate potential risks.
• Address legal considerations, such as intellectual property, data protection, and regulatory compliance.
• Design and apply an AI governance framework for the responsible deployment of AI in practical settings.

Topics Covered

• Foundations of Generative AI Governance
• Ethical Frameworks and AI Principles
• Privacy, Bias, and Fairness in AI Systems
• Governance Structures and Committees
• AI Legal Risks and Compliance
• Future of AI Governance and Implementation

Capstone Project

The capstone project serves as the culmination of your learning journey. You will leverage the skills acquired throughout the program to develop a fully functional generative AI solution, while addressing ethical considerations, bias reduction, and regulatory compliance. This project ensures a strong technical understanding of AI and the ability to apply governance principles in real-world contexts. By combining responsible AI practices with hands-on implementation, the capstone prepares you to confidently create AI solutions that are ethical, secure, and aligned with both business objectives and societal needs.

Industry Projects

• AI-Powered Business Intelligence Assistant (InsightForge)
• AI-Powered HR Chatbot
• AI-Driven Design Studio for Marketing Campaigns
• AI-Powered News & Information Assistant
• Python Adventure Game with GitHub Copilot
• Interactive Storytelling with ChatGPT
• Customer Order Analysis using Python

Elective Courses:

Elective 1: Microsoft Azure AI Fundamentals: Generative AI

Microsoft Azure offers a robust set of tools and services for building and deploying generative AI applications. This learning path covers the core concepts, underlying technologies, and ethical considerations of generative AI, giving you the foundation to explore its real-world applications.

Learning Outcomes:

• Learn how large language models form the backbone of generative AI.
• Explore the Azure AI Foundry portal to access cutting-edge generative AI tools.
• Understand how AI-powered copilots can boost productivity and efficiency.
• Examine the use of prompts and response tuning to optimize AI-generated content.
• Gain insight into Microsoft’s responsible AI principles and their role in ethical AI development.

Topics Covered

• Microsoft Azure AI Fundamentals: Generative AI
• Fundamentals of GenAI
• Explore Generative AI With Microsoft Copilot
• Plan, Prepare and Develop AI Solutions on Azure
• Responsible Generative AI
• Measure, Mitigate, and Identify Potential Harms
• Content Filters in Azure AI Studio

Elective 2: Build a Foundation to Extend Microsoft 365 Copilot

Microsoft 365 Copilot is a powerful AI tool that boosts productivity by integrating into daily workflows. This course teaches you how to expand Copilot’s capabilities using Microsoft Graph connectors and custom engine copilots. By the end, you’ll be able to make informed decisions on development strategies and efficiently manage agents within the Microsoft 365 admin center.

Learning Outcomes:

• Explore the architecture and design of Microsoft 365 Copilot.
• Learn to extend Copilot using agents, connectors, and plugins.
• Choose the best development approach for Copilot extensions while addressing security and privacy.
• Understand how Microsoft Graph connectors enhance search capabilities.
• Develop skills to manage agents effectively via the Microsoft 365 admin center.

Topics Covered

• Examine the Microsoft 365 Copilot design
• Microsoft 365 Copilot extensibility fundamentals
• Choose a Microsoft 365 Copilot extensibility development path
• Introduction to Graph connectors
• Introduction to declarative agents for Microsoft 365 Copilot
• Manage agents for Microsoft 365 Copilot

Elective 3: Masterclass

Participate in an exclusive online interactive masterclass led by industry experts and gain key insights into the latest generative AI technologies and innovations. Learn how AI is reshaping industries, boosting creativity, and driving progress. Join live discussions and explore practical applications to stay at the forefront of the AI revolution.

FREQUENTLY ASKED QUESTIONS

How is the program delivered?

The course is delivered entirely online through live virtual classes, offering an 80:20 blend of experiential training and theoretical learning. You'll engage in hands-on projects, case studies, and interactive sessions led by industry experts.

How is the class schedule looks like? Is there recordings?

The course typically spans about 16 weeks, with an estimated 8–10 hours of weekly live sessions There will be weekday and weekend class with variety of schedule. In between courses, there will be a lot of hands-on project to complete. Please email us to get the details schedule of the program. If you miss a class, you can always watch the recording.

NOTE:

Attendance cannot be marked by simply watching the session recordings. Attendance is recorded only when a learner joins the live session. Since these are university-affiliated programs, the criteria are more stringent, as they are set by the universities themselves. However recordings will be available . Learners can view the specific certificate criteria for each course directly on their LMS

Can I work full-time while enrolled in this program?

Yes, you can! The program schedule is designed to help busy professionals with full-time work. You can attend live instructor-led sessions which are mostly held on weekends at the designated time according to your schedule and then complete assignments/projects during your free time.

Can I change my cohort after enrolling in the program?

Yes. You’re entitled to one free cohort change within the first 60 days of enrollment. If you’re unable to continue with your current cohort after using this option, you may request an additional transfer for a fee. For guidance on the process or assistance with your request, please reach out to our support team.

How Is This Program Different from Other Online AI Courses or MOOCs?

Unlike typical self-paced courses, this program offers a university-backed, interactive learning model with over 70 hours of live sessions led by industry experts. Learners benefit from real-time engagement, dedicated mentorship, and peer collaboration, ensuring higher completion and deeper understanding while providing a University co-branded certificate, adding credibility and long-term career value.

What is an applied AI course?

An applied AI course focuses on using artificial intelligence to address real-world challenges. Rather than centering on theory, it emphasizes hands-on practice—building AI-powered applications, working with machine learning models, and leveraging tools like ChatGPT, Hugging Face, and OpenAI. Learners gain practical skills in areas such as automation, content creation, chatbots, and image generation, making it especially valuable for professionals who want job-ready expertise directly aligned with industry needs.

Eligibility Criteria for the Applied Generative AI Specialization

This program is open to learners with a bachelor’s degree in fields such as computer science, engineering, or mathematics. It is suitable for both beginners and professionals aiming to enhance their AI skills. While prior knowledge of programming or artificial intelligence is beneficial, it is not a strict requirement. The course is structured to guide you step by step, ensuring accessibility even for those new to AI and machine learning.

How efficient are the trainers?

The Applied AI course is led by seasoned industry professionals with expertise in machine learning, natural language processing, Google Cloud, and core computer science. Each trainer is selected for their real-world experience and proven ability to simplify complex concepts, ensuring you gain practical, hands-on knowledge from experts who have applied AI in real business scenarios.

What will be the Career Path After Completing the course?

With companies increasingly adopting AI, completing this Applied AI and ML course opens doors to a wide range of career opportunities. You’ll be equipped to pursue roles such as:

• AI Developer
• Machine Learning Engineer
• Data Scientist
• AI Consultant

As you gain experience, you can also progress into leadership positions focused on shaping AI strategy, driving innovation, and leading AI-driven transformation within organizations.

What is the Difference Between AI and Applied AI

Artificial Intelligence (AI) is the broader field that focuses on developing systems capable of human-like abilities such as learning, reasoning, and decision-making. It involves creating algorithms, models, and theories that enable machines to mimic intelligence.

Applied AI, on the other hand, is about putting those concepts into practice. It uses AI techniques and tools to solve real-world problems and deliver tangible outcomes.

Example:

• AI: Developing a machine learning model that can analyze medical scans.
• Applied AI: Using that model in hospitals to detect diseases from patient scans and reports, improving diagnosis speed and accuracy.