GenAI Practitioner: Build, Apply & Deploy Generative AI

The Expert in Generative AI course at DeepLearn Academy is designed to take learners beyond fundamentals and into real-world GenAI mastery. This program dives deep into Large Language Models, prompt engineering, RAG architectures, and multimodal AI systems. You will gain hands-on experience building intelligent GenAI applications using industry-standard tools and frameworks. The course emphasizes practical implementation, performance optimization, and responsible AI practices. Learners will work on real-world use cases and a guided capstone project to showcase applied expertise. This course is ideal for professionals ready to level up from AI foundations to GenAI specialization.

📚 Syllabus: Teaching Generative AI

🧭 Module 1: Introduction to Generative AI

• What is Generative AI? How is it different from traditional AI?
• Types of GenAI: Text, Image, Audio, Video, Code, Data
• Real-world use cases across industries
• Overview of foundational models (GPT, DALL·E, Stable Diffusion, etc.)

Assignments: Use ChatGPT or DALL·E to generate basic content
Tools: ChatGPT, Midjourney, Copilot demo

🤖 Module 2: Foundations of Machine Learning & Deep Learning

• Supervised vs. unsupervised vs. generative models
• Neural networks, transformers, attention mechanism
• Autoencoders, GANs (Generative Adversarial Networks)
• Intro to LLMs (Large Language Models)

Hands-on: Build a simple GAN using TensorFlow or PyTorch
Tools: TensorFlow, PyTorch, scikit-learn

📄 Module 3: Language Models & NLP

• Tokenization, embeddings, attention
• Training vs. fine-tuning vs. prompting
• Transformers and the evolution to GPT
• Transfer learning in NLP (BERT, GPT, T5, etc.)

Projects: Sentiment analysis, summarization, question-answering
Tools: HuggingFace Transformers, OpenAI API, LangChain

🧠 Module 4: Working with LLMs

• Prompt engineering techniques
• Temperature, top-k, top-p (nucleus) sampling
• Few-shot, zero-shot, and chain-of-thought prompting
• RAG (Retrieval-Augmented Generation)

Projects: Create a chatbot or text-based assistant
Tools: OpenAI API, LangChain, LlamaIndex, Pinecone

🖼️ Module 5: Image, Audio & Multimodal Generation

• DALL·E, Stable Diffusion, Midjourney (images)
• AudioGen, ElevenLabs (audio), MusicGen
• Sora (video), Synthesia (avatar video)
• Multimodal models (GPT-4o, Gemini, Claude Opus)

Assignments: Generate art, avatars, or synthetic audio
Tools: Runway ML, Hugging Face Spaces, Replicate

🧪 Module 6: Building GenAI Applications

• Using APIs and SDKs (OpenAI, Claude, Google, Meta)
• Building with LangChain and LlamaIndex
• Adding memory, context windows, agents
• Web integration and UI (Streamlit, Flask, Gradio)

Projects: Build a GenAI-powered app (e.g. legal assistant or writing coach)

📊 Module 7: Evaluation & Optimization

• Metrics for evaluating GenAI (BLEU, ROUGE, perplexity, human eval)
• Prompt tuning vs. fine-tuning
• Performance and cost optimization
• Model interpretability and debugging

🛡️ Module 8: Ethics, Risks & Responsible AI

• Hallucinations, bias, disinformation
• Deepfakes and detection techniques
• Fairness, accountability, transparency
• Legal & regulatory considerations (e.g. GDPR, copyright, AI Act)

Discussion: Case studies of GenAI misuse and governance policies

🏁 Capstone Project

Build and deploy a real-world Generative AI application from scratch:

• Select domain (e.g., education, finance, health, media)
• Ingest custom data (RAG, fine-tuning optional)
• Use API + front-end + deployment
• Present results and lessons learned

🎓 Optional Tracks by Role:

• For developers: APIs, apps, tools, LangChain
• For researchers: Theory, training models, optimization
• For business leads: Use cases, strategy, policy, ROI

🛠 Tools and Platforms to Include: