An 8-Week Hands-On Learning Path
Learn Generative AI by Building Practical Applications
A structured path for beginners who want to understand Generative AI, build portfolio-ready applications, and develop the confidence to explain their work in interviews.
What You'll Finish With
By the end of the course, you'll have:
- ✓A structured understanding of modern Generative AI concepts
- ✓Practical experience with RAG, AI agents, multimodal AI, and model customization
- ✓Multiple guided AI applications
- ✓A complete capstone project
- ✓A portfolio-ready collection of work
- ✓Interview and resume guidance
- ✓On-demand video lessons to revisit when needed
- ✓A certificate after completing the stated course and capstone requirements
How the Course Works
You'll learn
Understand the concepts, tools, and workflows behind modern Generative AI.
You'll build
Create guided applications and assignments that turn concepts into practical experience.
You'll be able to explain
Present the problem, approach, tools, decisions, and limitations behind your projects.
The Eight-Week Curriculum
Tap a week to see exactly what you'll learn, build, and be able to explain.
Week 1Foundations of Generative AI
You'll learn
- AI and Machine Learning fundamentals
- Supervised and unsupervised learning
- Neural networks
- NLP and Generative AI
- Attention mechanisms
- The Generative AI ecosystem
- Python fundamentals
- LangChain
- Prompt engineering
- Different instruction types
- Code walkthroughs
You'll build
- Beginner-friendly Generative AI experiments
- Prompt-based applications
- Small code walkthrough projects
You'll be able to explain
- How Generative AI differs from traditional Machine Learning
- Why attention mechanisms matter
- How prompts influence model output
- The basic structure of a Python AI application
- Where LangChain fits into the ecosystem
Week 2Retrieval-Augmented Generation
You'll learn
- RAG fundamentals
- Ingestion and retrieval
- Embeddings
- Cosine similarity
- Vector databases
- How retrieval improves language model responses
You'll build
- A document question-answering application
- A retrieval workflow using a knowledge source
- A practical RAG application
You'll be able to explain
- Why language models need external knowledge
- The difference between ingestion and retrieval
- How embeddings and vector databases support semantic search
- How retrieved context improves responses
Week 3AI Agents and Agentic Workflows
You'll learn
- AI agents
- Agentic workflows
- OpenAI Agent Builder
- n8n workflows
- ReAct agents
- CrewAI
- Human-in-the-loop workflows
- MCP fundamentals
You'll build
- An agentic workflow using OpenAI Agent Builder
- An automation workflow using n8n
- A multi-step AI workflow
- A practical agent application using CrewAI concepts
You'll be able to explain
- The difference between assistants, agents, and agentic workflows
- How agents select actions
- When human approval is needed
- How tools extend agent capabilities
- Where MCP fits into agent applications
Week 4Multimodal AI
You'll learn
- Text, image, audio, and video AI
- Text-to-speech
- Speech-to-text
- Wispr Flow
- Text-to-image models
- HeyGen
- Suno
You'll build
- A speech or audio workflow
- A multimodal AI experiment
- Practical content-generation applications
You'll be able to explain
- What makes an application multimodal
- Speech recognition versus speech generation
- How different AI modalities work together
- Which modality fits different use cases
Week 5Creating and Deploying AI Applications
You'll learn
- Prompt templates
- GitHub
- Streamlit
- Cursor IDE
- MCP-based applications
- Supabase
- AI-assisted development
- Vercel V0
You'll build
- A functional AI application with an interface
- A GitHub and Streamlit project
- An application connected to Supabase
- A deployed AI application
You'll be able to explain
- How an AI app moves from idea to deployment
- The role of GitHub
- How Streamlit creates an interactive app
- Why an AI app may need a database
- The difference between a prototype and a deployed application
Week 6Advanced Generative AI Concepts
You'll learn
- Model customization
- Fine-tuning
- GANs
- VAEs
- Diffusion
- Reinforcement Learning
- Ollama and local language models
You'll build
- A model customization or fine-tuning experiment
- A local AI experiment using Ollama
- Demonstrations comparing Generative AI techniques
You'll be able to explain
- Prompting versus retrieval versus fine-tuning
- The purpose of fine-tuning
- GANs, VAEs, and diffusion models
- Reinforcement Learning fundamentals
- Benefits and trade-offs of local models
Week 7Capstone Project
You'll learn
- Problem selection
- Application scope
- Combining course skills
- Project review
- Presenting technical decisions
You'll build
- A complete AI application based on a chosen problem
- A portfolio-ready capstone project
- A project presentation or walkthrough
You'll be able to explain
- The problem being solved
- Why a particular AI approach was chosen
- How the application works
- Tools, models, and data sources used
- Limitations and future improvements
- The project in a portfolio, resume, or interview
Completion outcome
- Project review
- Course completion certificate
Week 8 · BonusCareer and Productivity Toolkit
You'll learn
- AI interview guidance
- Lovable for portfolio creation
- ResumeWorded for resume improvement
- NotebookLM for understanding information
- Gamma for presentations
You'll build
- A presentation of AI projects
- A structured portfolio
- Improved resume content
- Clearer project explanations
You'll be able to explain
- How to present an AI project
- How to describe technical decisions
- How to turn projects into resume bullets
- How to use AI productivity tools responsibly
Course Benefits
Structured learning path
Follow an eight-week sequence instead of jumping between disconnected tutorials.
Hands-on application building
Practice by creating AI applications, not just watching explanations.
On-demand video lessons
Learn at a time that fits your schedule and revisit lessons whenever you need to review a concept.
Portfolio-ready capstone
Apply your learning to a complete project based on a real-world problem.
Portfolio and interview guidance
Learn how to present your projects, describe your technical decisions, and improve your resume and LinkedIn profile.
Certificate after completion
Complete the stated course and capstone requirements to receive a certificate you can showcase on your resume and LinkedIn.
Ready to Build Your AI Portfolio?
Follow a structured path, build practical applications, complete a capstone, and develop the confidence to discuss your work in interviews.