1 Introduction to course (73.36 MB)
21 Use Cases and Conclusion (46.97 MB)
2 Generative AI without RAG Why RAG (24.61 MB)
3 What is RAG RAG Process (25.69 MB)
4 What is NLP (16.58 MB)
5 POS , NER , Chunking, BoW, TF-IDF and Embedding (65.17 MB)
6 Tokenization, Stemming and Lemmatization (22.43 MB)
7 Evaluation of NLP (30.57 MB)
8 Transformer Model (82.72 MB)
10 Create simple streamlit chatbot (43.07 MB)
9 Setup VS code , Python, Neo4j, Streamlit, PIP packages (149.97 MB)
11 What is vector RAG (23.96 MB)
12 Develop vector RAG with Groq API and Langchain (116.9 MB)
13 What is Graph RAG (37.44 MB)
14 Implement Graph RAG chatbot to build and show graph with Neo4j (162.35 MB)
15 Implement hybrid search with Graph RAG and Neo4j (218.4 MB)
16 Understand adaptive or self-reflective flow (10.63 MB)
17 Implement Self-reflective RAG chatbot with Langgraph (293.17 MB)
18 Flow of Ranking RAG and LangChain Python coding (148.61 MB)
19 Autogen RAG Agentic RAG (85.48 MB)