Generative AI Training Curriculum
Build practical skills in Artificial Intelligence, Large Language Models, RAG, LangChain, Vector Databases, and AI Application Development.
1. Introduction to Generative AI
- Understand the fundamentals of Artificial Intelligence, Machine Learning, and Generative AI.
- Learn how Generative AI creates text, images, code, audio, and other digital content.
- Explore real-world applications of Generative AI in the IT industry.
2. Python for Generative AI
- Learn Python programming fundamentals required for AI development.
- Work with libraries and tools used in data processing and AI applications.
- Develop practical Python programs for AI-based solutions.
3. Machine Learning and Deep Learning Basics
- Understand the concepts of Machine Learning and Neural Networks.
- Learn how deep learning models process and generate information.
- Explore the foundation behind modern Generative AI systems.
4. Large Language Models (LLMs)
- Understand how Large Language Models process and generate human-like text.
- Explore concepts such as tokens, embeddings, context windows, and transformers.
- Learn to work with popular LLM-based applications and platforms.
5. Prompt Engineering
- Learn how to design effective prompts for Generative AI models.
- Understand zero-shot, one-shot, and few-shot prompting techniques.
- Create structured prompts for text generation, analysis, coding, and automation.
6. Generative AI Application Development
- Build practical AI-powered applications using Python and APIs.
- Develop chatbots, document analysis systems, and intelligent assistants.
- Learn how to integrate Generative AI into web and business applications.
7. Retrieval-Augmented Generation (RAG)
- Understand how RAG improves AI responses using external knowledge sources.
- Learn document loading, text chunking, embeddings, and vector databases.
- Build intelligent PDF and document-based question-answering applications.
8. LangChain and AI Frameworks
- Learn to use frameworks such as LangChain for developing AI applications.
- Connect LLMs with documents, databases, APIs, and external tools.
- Build conversational AI and knowledge-based applications.
9. Vector Databases and Embeddings
- Understand semantic search and vector embeddings.
- Work with vector databases such as Chroma and FAISS.
- Build efficient systems for searching and retrieving relevant information.
10. Real-World Projects and Career Skills
- Develop industry-oriented Generative AI projects and practical applications.
- Build AI chatbots, document assistants, and automated business solutions.
- Gain the skills required to begin a career in Generative AI and AI application development.