1.66 GB | 00:20:36 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
1 -Introduction to course  (73.36 MB)
1 -RAG Code  (521.25 KB)
1 -Use Cases and Conclusion  (46.97 MB)
1 -Generative AI without RAG  Why RAG  (24.61 MB)
2 -What is RAG RAG Process  (25.69 MB)
1 -What is NLP  (16.58 MB)
2 -POS , NER , Chunking, BoW, TF-IDF and Embedding  (65.17 MB)
3 -Tokenization, Stemming and Lemmatization  (22.43 MB)
4 -Evaluation of NLP  (30.57 MB)
5 -Transformer Model  (82.72 MB)
1 -Setup VS code , Python, Neo4j, Streamlit, PIP packages  (149.97 MB)
2 -Create simple streamlit chatbot  (43.07 MB)
1 -What is vector RAG  (23.96 MB)
2 -Develop vector RAG with Groq API and Langchain  (116.9 MB)
2 -RAG Code  (521.25 KB)
1 -What is Graph RAG  (37.44 MB)
2 -Implement Graph RAG chatbot to build and show graph with Neo4j  (162.35 MB)
3 -Implement hybrid search with Graph RAG and Neo4j  (218.4 MB)
3 -RAG Code  (521.25 KB)
4 -Vector RAG vs Graph RAG  (21.04 MB)
1 -Understand adaptive or self-reflective flow  (10.63 MB)
2 -Implement Self-reflective RAG chatbot with Langgraph  (293.17 MB)
2 -RAG Code  (521.25 KB)
1 -Flow of Ranking RAG and LangChain Python coding  (148.61 MB)
1 -Autogen RAG Agentic RAG  (85.48 MB)]
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