Applied Generative AI Specialization in collaboration with Purdue University

Applied Generative AI Specialization

In collaboration with Purdue University

• Build and Launch GenAI-Powered Applications with the Applied AI Program

• Join interactive masterclasses led by industry experts

• Create GenAI and Agentic AI applications through 7+ practical projects

• 16 Week length program (8-10/week weekend classes)
• Ask us for the next cohort and schedule details!

In collaboration with Purdue University Online and partnership with Microsoft Azure, this program equips professionals with the expertise needed to thrive in today’s fast-changing AI landscape. It combines academic excellence with practical learning through live classes, real-world projects, and hands-on experience with tools like ChatGPT, LangChain, Azure AI Studio, and DALL·E.

The curriculum covers everything from core AI fundamentals to advanced Generative AI, agentic systems, and governance frameworks. Graduates emerge ready to lead in a GenAI-powered era, backed by recognized credentials from Purdue University Online and Microsoft.

After completing the program, you’ll join Purdue’s prestigious alumni network 

Key Features

• Course and material are in English
• in collaboration with Purdue University Online
• Intermediate to advanced level
• 16 weeks duration of live classroom by industry experts (8-10 hours/week weekend classes) 
• 70+ hours of live classes and mentor-led project support
• 200+ hours of study time and practice recommended
• Flexible learning with session recordings and 24/7 access
• Hands-on learning through 7+ real-world projects and a capstone project.
• Master in-demand skills like LLM fine-tuning, prompt engineering, and AI governance.
• Comprehensive curriculum covering AI Literacy, Agentic Frameworks, and Generative AI.
• Microsoft Course completion certificate hosted on the Microsoft Learn portal
• Networking benefits via Purdue’s Alumni Association
• Program completion certificate from Purdue University Online. 

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 Purdue University

Purdue University is a leading public research university known for creating practical solutions to some of today’s most pressing problems. Recognized by U.S. News & World Report as one of the top 10 Most Innovative Universities in the U.S. for four consecutive years, Purdue is at the forefront of groundbreaking research and innovation.

What added value does Purdue University contribute to the program?

The program curriculum is designed and reviewed with the assistance of the university, which gives the program quality legitimacy and a co-branded certificate of completion. Please be aware that the live classes are not held by actual University faculty staff but by many experienced Industry experts who are suitable for each topic.

Skills Covered

• Python Programming
• Prompt Engineering
• AI Literacy & Generative AI Fundamentals
• Large Language Models (LLMs)
• Agentic AI & Autonomous AI Architectures
• LangChain Workflow Design
• Retrieval-Augmented Generation (RAG)
• LLM Fine-Tuning & Customization
• Stable Diffusion & AI Image Generation
• Transformers & Attention Mechanisms
• Variational Autoencoders (VAEs)
• Generative AI Application Development
• LLM Benchmarking & Evaluation
• Generative AI Governance & Ethics

Learning Objective

• Build AI-powered business intelligence assistants to generate insights and recommendations.
• Gain practical skills in Python using Jupyter Notebook and Google Colab.
• Develop strong AI literacy, including machine learning types and deep learning concepts.
• Apply advanced LLM techniques such as RAG, fine-tuning, and prompt engineering.
• Understand key Generative AI models like neural networks, GANs, and transformers.
• Create autonomous AI systems using agentic frameworks like LangChain.
• Design and deploy AI copilots and chatbots with Microsoft Azure AI Studio and Copilot Studio.
• Implement real-world AI applications such as HR assistants and generative design tools (DALL·E, Gradio UI).
• Learn AI governance, ethics, compliance, and strategies to build secure, reliable systems.
• Evaluate and optimize LLM outputs through attention mechanisms, benchmarking, and risk assessment.
• Work with leading AI tools such as ChatGPT, Gemini, Claude, Hugging Face, and Stable Diffusion.
• Gain hands-on skills in data preprocessing, visualization, feature engineering, and model fine-tuning for domain-specific applications.
• Understand embeddings, vector databases, and open-source AI repositories to enhance AI search and stay current with new models.
• Deploy AI solutions on cloud platforms for scalable business applications.
• Build a strong AI portfolio through real-world projects and industry-relevant case studies.
• Earn dual credentials with a Purdue University Online certificate and a Microsoft certification via Microsoft Learn, along with access to Purdue’s alumni network.

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 Fundamentals (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 Capabilities
7. Generative AI Governance
8. Capstone Project

Electives

1. Microsoft Azure AI Fundamentals: Generative AI
2. Microsoft Copilot Foundations
3. Purdue Academic Masterclass

COURSE CONTENT DETAILS

Course 1: Python Fundamentals

Develop core Python skills that form the foundation of your learning path. Apply Python to build AI and ML algorithms, perform data analysis, and create intelligent systems with efficiency.

Learning Outcomes

• Set up Python and work with its integrated development environment (IDE).
• Apply Python basics such as identifiers, indentation, and comments effectively.
• Understand and use different types of loops in Python.
• Learn the fundamentals of multi-threading.
• Explain Python methods, attributes, and access modifiers.
• Explore the key benefits and applications of Python.
• Gain hands-on experience with Jupyter Notebook.
• Work with Python data types, operators, and string functions.
• Understand variable scope within functions.

Topics Covered

• Fundamentals of Programming 
• Introduction to Python Programming
• Python Data Types and Operators 
• Python Functions
• Conditional Statements and Loops in Python
• Threading

Course 2: AI Literacy

Establish a solid grounding in Generative AI and Machine Learning by learning core principles, essential algorithms, and real-world applications. Explore deep learning, large language models, and AI-driven tools to build practical skills in creating and deploying AI solutions.

Learning Outcomes

• Distinguish between AI, machine learning, deep learning, and generative AI.
• Understand supervised, unsupervised, and reinforcement learning with their real-world applications.
• Study generative AI models including neural networks, GANs, and transformers.
• Learn how LLMs drive chatbots and explore models like ChatGPT, Gemini, Claude, and Falcon.
• Explore image generation methods with GANs, diffusion models, and VAEs, and practice with tools like DALL·E 2 and Stable Diffusion.
• Gain hands-on experience in video generation using AI-driven platforms.
• Discover open-source resources like Hugging Face and explore AI/prompt marketplaces such as PromptBase.
• Build skills in prompt engineering to optimize chatbot interactions and AI search.
• Experiment with OpenAI Playground settings, including temperature and sampling techniques.

Topics Covered

• Fundamentals of Machine Learning and Generative AI
• Machine Learning Types: Supervised, Unsupervised, and Reinforcement Learning
• Generative AI Models: Neural Networks, GANs, and Transformers (GPT and beyond)
• Large Language Models (LLMs) and Chatbots: ChatGPT, Gemini, Claude, Falcon, etc.
• Video Generation: Architectures (GANs, Diffusion Models, Transformers) with tools like Runway ML, Synthesia, and Gen-2
• Image Generation: GANs, Diffusion Models, VAEs with tools such as DALL·E 2, Stable Diffusion, and MidJourney
• Open-Source AI Ecosystem: Hugging Face and AI marketplaces
• Prompt Engineering: Crafting chatbot prompts, image generation prompts, and experimenting with OpenAI Playground

Course 3: Advanced Generative AI: Models and Architecture

Harness the creative power of AI with this Generative AI course. Explore generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), while gaining insights into cutting-edge Large Language Models (LLMs) and Transformer architectures. Develop a deeper understanding of attention mechanisms and how they elevate the performance of AI systems.

Learning Outcomes

• Learn the significance of generative AI and its real-world applications.
• Recognize and explain different types of generative AI models.
• Study the structure and use cases of large language models (LLMs).
• Apply Variational Autoencoders (VAEs) for data generation and anomaly detection.
• Understand Generative Adversarial Networks (GANs) and how they are used in practice.
• Explore attention mechanisms, transformer architectures, and their practical implementations.

Topics Covered

• Overview of Generative Models
• Architecture of Large Language Models (LLMs)
• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)
• Attention Mechanisms and Transformer Models

Course 4: Advanced Generative AI: Building LLM Applications

Gain expertise in LangChain workflow design to effectively manage language generation processes. Advance your skills in prompt engineering to create precise and impactful prompts. Learn how to develop applications powered by Large Language Models (LLMs) for various use cases. Explore fine-tuning techniques to adapt LLMs for specialized tasks and domains. Conclude with benchmarking practices to assess and compare LLM performance against industry standards.

Learning Outcomes

• Create LangChain workflows for building generative AI solutions
• Use advanced prompt engineering methods to achieve customized results
• Build LLM-based applications and fine-tune them for targeted use cases
• Assess LLM performance in tasks like summarization, Q&A, translation, chatbots, and sentiment analysis

Topics Covered

• LangChain and Workflow Design 
• Advanced Prompt Engineering Techniques
• LangChain for LLM Application Development
• Benchmarking and Evaluating of LLM Capabilities
• LLM Fine-Tuning and Customization

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

After mastering foundational LLM applications, advance to the next level of generative AI with agentic architectures and protocol-driven tool integrations. This course offers practical experience in building intelligent agents and connecting them through secure, vendor-independent standards using MCP. Learn to design agents, create orchestration flows, and build tool-agnostic systems with platforms like LangGraph, AutoGen, CrewAI, and MCP.

Learning Outcomes

• Understand the development of Agentic AI and its main architectural elements
• Learn perception modules, cognitive engines, and action execution processes
• Apply LangGraph for orchestration, task routing, and automation workflows
• Build and customize AutoGen agents for reasoning and collaborative tasks
• Explore how MCP supports standardized, cross-platform integrations
• Implement secure protocols, SDKs, and governance-oriented practices
• Organize agent teams and task pipelines using CrewAI

Topics Covered

• Fundamentals of Agentic AI: Key attributes and real-world applications
• LLM Agent Design: Perception, cognitive engines, and action modules
• LangGraph: Orchestration methods with task nodes and parallel routing
• AutoGen: Flexible multi-agent systems and collaborative workflows
• CrewAI: Building agent teams with toolchains, tasks, and execution pipelines
• Best Practices: Role-based access, secure data usage, and compliance standards
• MCP: Ensuring interoperability, messaging protocols, and secure SDK integration

Course 6: Advanced Generative AI: Image Generation Capabilities

Unlock the advanced potential of Generative AI for image creation in this specialized course. Master Stable Diffusion and denoising methods to generate clear, high-quality visuals from noisy inputs. Explore Shared Embedding Systems to seamlessly integrate and represent diverse image features. Gain proficiency in Contrastive Learning techniques to improve model performance by effectively utilizing data similarities and differences.

Learning Outcomes

• Learn and apply stable diffusion techniques to create clear, high-quality images from noisy or incomplete inputs
• Develop skills in denoising methods to improve image accuracy and detail
• Use shared embedding systems to effectively represent a wide range of image features
• Gain practical experience in applying contrastive learning to boost model accuracy and efficiency

Topics Covered

• Stable Diusion Denoising
• Autoencoders in Generative AI
• Shared Embedding Spaces
• Contrastive Learning Techniques

Course 6: Generative AI Governance

Examine the importance of Generative AI governance, addressing key challenges, ethical principles, governance models, and risk management. Learn how to embed governance into AI projects while keeping pace with regulatory developments and emerging trends in the field.

Learning Outcomes

• Understand why governance is critical in Generative AI
• Address governance challenges such as ethics, accountability, and regulations
• Learn the role of governance in reducing risks within AI projects
• Explore ethical principles, dilemmas, and responsible AI development
• Apply fairness, bias mitigation, and privacy protection as ethical practices
• Build governance frameworks and committees with defined roles and best practices
• Study risk management strategies with real-world Generative AI case examples
• Integrate governance across the AI project lifecycle, from data sourcing to auditing
• Stay updated on future trends in governance, including regulations and ethical innovation
• Discover career opportunities in Generative AI governance

Topics Covered

• Introduction to Generative AI Governance
• Ethical Frameworks and Principles
• Governance Structures and Committees 
• Risk Management in AI Projects
• AI Project Lifecycle and Governance 
• Future Trends in AI Governance

Capstone Project

Conclude the program by applying your skills in a practical, industry-focused capstone project that integrates all course learnings into a portfolio-ready showcase.

Elective Courses:

Elective 1: Microsoft Azure AI Fundamentals: Generative AI

This Microsoft Learn path introduces the fundamentals of Generative AI, covering core concepts, techniques, and ethical practices to help you build a strong foundation for practical applications.

Throughout the journey, you will:

• Learn how large language models serve as the backbone of Generative AI
• Explore how Azure OpenAI Service enables access to cutting-edge AI technologies
• Understand how generative AI tools like copilots enhance productivity and efficiency
• Discover how prompts and responses can be refined and fine-tuned
• Examine Microsoft’s Responsible AI principles and their role in advancing ethical AI

Elective 2: Microsoft Copilot Foundations

Begin by exploring the Microsoft Copilot Studio environment and interface to design and manage copilots, develop topics, and boost productivity with generative AI. Gain experience with Azure AI Studio to learn its capabilities, ground language models, enable data searchability, and build copilots using prompt flow. Apply Retrieval-Augmented Generation (RAG) to enhance response accuracy, and create a custom copilot with your own data for hands-on practice.

You will learn to:

• Build and manage copilots in Microsoft Copilot Studio
• Publish copilots and evaluate their performance
• Understand the purpose and features of Azure AI Studio
• Develop a RAG-powered copilot solution
• Ground language models for accuracy and reliability
• Create copilots using prompt flow

Elective 3: Purdue Academic Masterclass

Join an interactive online masterclass to gain valuable insights into the latest advancements and techniques in Generative AI.

Industry Projects

• Personal Expense Tracker: Create a personal finance tracker with categorized expenses, monthly budgeting, and file-based data storage. Includes a menu-driven interface for easy navigation.
  
  

• Task Manager with User Authentication: Develop a task management system with user registration and login. Users can add, view, complete, and delete tasks, with persistent storage handled through file management.
  
  

• AI-Powered HR Assistant: Build an AI assistant leveraging OpenAI’s GPT and Gradio UI to extract and answer questions from Nestlé’s HR policy documents, improving HR information accessibility.
  
  

• AI-Driven Design Generator: Design a platform that converts text prompts into visual content using OpenAI’s DALL·E and Gradio UI, streamlining marketing and creative workflows.
  
  

• AI-Powered Business Intelligence Assistant: Develop InsightForge, a business intelligence tool powered by RAG and LLMs to analyze data, detect trends, and deliver insights with interactive visualizations.
  
  

• Image Generation App with LangChain: Create an application integrating LangChain with OpenAI’s DALL·E to transform text descriptions into realistic images and artwork.
  
  

• Fine-Tuned Falcon7 Personalized LLM: Experiment with Falcon-7b by fine-tuning it for custom text generation tasks. Implement personalization techniques by training with task-specific examples.

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.

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.