COURSE OUTLINE 

1. Introduction
2. Python control flow
3. Data structure using python
4. Functions in python
5. Importing, creating modules and packages
6. File handling in python
7. Exception handling
8. Oops classes and objects
9. Streamlet with python
10. Machine learning for NLP(prerequisites)
11. Deep learning for NLP(prerequisites)
12. Simple RNN in-depth intuition
13. ANN Project implementation
14. End to end deep learning project with simple RNN
15. LSTM and GRU end to end deep learning project-predicting next word
16. bidirerctional RNN indepth intuition
17. encoder and decoder | sequence to sequence architecture
18. attention machanisem-seq2seq networks
19. transformers
20. introduction to generative AI and LLM models
21. getting started with langchain and OpenAI
22. important components and modules in langchain
23. getting started with Open AI and Ollama
24. building basic LLM application using LCEL(Langchain expression Language)
25. building chatbot with conversation history
26. end to end Q & A chatbot GEN AI app with
27. RAG document Q & A with GROQ API and Llama3
28. Conversational Q & A chatbot – chat with PDF along with chat history
29. Search engine with langchain tools and agents
30. Gen AI project-chat with SQL DB with Langchain SQL Toolkit and Agentype
31. Text summarization with longchain
32. Gen AI project -Youtube video and Website URl content summarization
33. Text to math problem solver using google Gemma 2
34. Huggingface and langchain integration
35. Pdf query RAG with langchain and astraDB
36. Multilanguage code assistant using codelama
37. Deployment of Gen AI apps in streamlet and huggingspace
38. Generative AI with Aws(bonus)
39. Getting started with Nvidia NIM and Langchain
40. Creating multi AI agents using crewAI for real world usecases
41. Hybrid search RAG with Vector Database and Langchin
42. Introduction to Graph Database and cypher query language with langchain
43. Practical implementation with graphdb with langchain
44. Detail intuition and implementation of finetuning LLM models
45. End to end finetuning LLM models with lamini platform
46. Building stateful, multi-actor applications using Lang graph
47.