1.5: Variables In Python

1.6: Basics DataTypes In Python

1.7: Operators In Python

3.1: Lists and List Comprehension In Python

3.2: Tuples In Python

3.3: Dictionaries In Python

3.4: Real World Use cases Of List

4.1: Real World Use cases Of List

4.2: More Coding Examples With Functions

4.3: Lambda Function In Python

4.4: Map Function In Python

4.5: Filter Functions In Python

5.1: Import Modules And Packages In Python

5.2: Standard Libraries Overview In Python

6.1: File Operations With Python

6.2: File Operations With Python

7.1: Exception Handling With try except else and finally blocks

8.1: Classes And Objects In Python

8.2: Single And Multiple Inheritance

8.3: Polymorphism In OOPS

8.4: Encapsulation In OOPS

8.5: Abstraction In OOPS

8.6: Magic Methods In Python

8.7: Operator Overloading In Python

9.1: Getting Started With Streamlit

9.2: Example Of ML APP With Streamlit

10.1: Roadmap To Learn NLP

10.2: Practical Usecases Of NLP

10.3: Tokenization and Basic Terminologies

10.4: Tokenization Practicals

10.5: Text Preprocessing Stemming Using NLTK

10.6: Text Preprocessing Lemmatization

10.7: Text Preprocessing Stopwords

10.8: Parts Of Speech Tagging Using NLTK

10.9: Named Entity Recognition

10.10: Whats Next

10.11: One Hot Encoding

10.12: Advantages and Disadvantages of OHE

10.13: Bag Of Words Intuition

10.14: Advantages and Disadvantages Of BOW

10.15: BOW Implementation Using NLTK

10.16: N Grams

10.17: N gram Implementation Using NLTK

10.18: TF-IDF Intuition

10.19: Advantages and Disadvantages OF TFIDF

10.20: TFIDF Practical Implementation

10.21: Word Embeddings

10.22: Word2vec Intuition

10.23: Word2vec CBOW Detailed Explanation

10.24: Skip Gram Indepth Intuition

10.25: Advantages OF Word2vec

10.26: Word2vec Practical Implementation

11.1: Introduction To NLP In Deep Learning

11.2: ANN VS RNN

12.1: RNN Forward Propagation With Time

12.2: Simple RNN Backward Propagation

12.3: Problems With RNN

13.1: Discussing Classification Problem Statement And Setting Up Vs Code

13.2: Feature Transformation Using Sklearn With ANN

13.3: Step By Step Training With ANN With Optimizer and Loss Functions

13.4: Prediction With Trained ANN Model

13.5: Integrating ANN Model With Streamlit Web APP

13.6: Deploying Streamlit web app with ANN Model

13.7: ANN Regression Practical Implementation

13.8: Finding Optimal Hidden Layers And Hidden Neurons In ANN

14.1: Problem Statement

14.2: Getting Started With Embedding Layers

14.3: Implementing Word Embedding With Keras TensorFlow

14.4: Loading And Understanding IMDB Dataset And Feature Engineering

14.5: Training Simple RNN With Embedding Layers

14.6: Prediction From Trained Simple RNN

14.7: End To End Streamlit Web App Integrated With RNN And Deployment

15.1: Why LSTM RNN

15.2: LSTM RNN Architecture

15.3: Forget Gate In LSTM RNN

15.4: Input Gate And Candidate Memory In LSTM RNN

15.5: Output Gate In LSTM RNN

15.6: Training Process In LSTM RNN

15.7: Variants Of LSTM RNN

15.8: GRU RNN Indepth Intuition

16.1: Discussing Problem Statement

16.2: Data Collection And Preprocessing

16.3: LSTM Neural Network Model Training

16.4: Prediction From LSTM Model

16.5: Streamlit Webapp Integration With LSTM Trained Model

16.6: GRU RNN Variant Implementation

17.1: Bidirectional RNN- Why To Use It?

18.1: Indepth Intuition OF Encoder And Decoder-Sequence to Sequence Architecture

18.2: Problems With Encoder and Decoder

19.1: Attention Mechanism Indepth Architecture Explanation

20.1: Plan Of Action

20.2: What and Why To Use Transformers

20.3: Understanding The basic Architecture Of Encoder

20.4: Self Attention Layer Working

20.5: Multi Head Attention

20.6: Feed Forward Neural Network With Multi Head Attention

20.7: Positional Encoding Indepth Intuition

20.8: Layer Normalization

20.9: Layer Normalization Examples

20.10: Complete Encoder Transformer Architecture

20.11: Decoder-Plan Of Action

20.12: Decoder-Masked Multi Head Attention

20.13: Encoder and Decoder Multi Head Attention

20.14: Decoder Final Linear And Softmax Layer

21.1: What is Generative AI- AI Vs ML Vs DL Vs Generative AI

21.2: How Open AI ChatGPT or LLama3 LLM Models are Trained

21.3: Evolution OF LLM Models

21.4: All LLM Models Analysis

22.1: Complete Langchain Ecosystem

22.2: Creating Virtual Environment

23.1: Getting Started With Langchain And OpenAI

24.1: Introduction To Basic Components And Modules In Langchain

24.2: Data Ingestion With Documents Loaders

24.3: Text Splitting Techniques-Recursive Character Text Splitter

24.4: Text Splitting Technique-Character Text splitter

24.5: Text Splitting Technique-HTML Header Text Splitter

24.6: Text Splitting Technique-Recursive Json Splitter

24.7: Introduction To OpenAI Embedding

24.8: Ollama Embeddings

24.9: Huggingface Embeddings

24.10: VectorStores-FAISS

24.11: VectorStore And Retriever- ChromaDB

24.12: 2 Going Forward

25.1: Building Important Components Of Langchain

25.2: Building GENAI Apps

25.3: Understanding Retrievers and Chains

25.4: Introduction To Ollama And Set Up

25.5: Simple Gen AI App Using Ollama

25.6: Tracking Gen AI App Using Langsmith

26.1: Getting Started With Open source Models Using Groq API

26.2: Building LLM, Prompt And Stroutput Chains With LCEL

26.3: Deploy Langserve Runnable And Chain As API

27.1: Building Chatbot With Message History Using Langchain

27.2: Working With Prompt Template And Message Chat History Using Langchain

27.3: Managing the Chat Conversation History Using Langchain

27.4: Working With VectorStore And Retriever

28.1: Building Conversational Q&A Chatbot With Message History

29.1: Introduction To The Q&A Chatbot

29.2: Creating Virtual Environment

29.3: Creating Prompt Template And Integrating Open AI API

29.4: Creating Streamlit Web App and Integrating Response With OpenAI API

29.5: Q&A Chatbot With Ollama And Open Source Models

30.1: Introduction To Groq Cloud And LPU Inference Engine

30.2: RAG Document Q&A With GROQ API And LLama3

31.1: Demo of the Conversational Q&A Chatbot

31.2: End To End Conversational Q&A Chatbot Implementation

32.1: Introduction To Tools And Agents

32.2: Creating Tools Using Langchain

32.3: Executing Tools And LLM with Agent Executors

32.4: End To End Search Engine GEN AI App using Tools And Agent With Open Source LLM

33.1: Demo of the Project

33.2: Preparing the Data For SQlite3 Database

33.3: Preparing The Data For My SQL Database

33.4: Creating the Streamlit Web app and Configuring the Databases

33.5: Integrating Web App With Langchain SQL Toolkit And Agent type

34.1: Introduction To text summarization With Langchain

34.2: Stuff Chain And Map Reduce Text Summarization Indepth Intuition

34.3: Stuff And Map Reduce Summarization Implementation

34.4: Refine Chain Summarization Intuition And Implementation

35.1: End To End Project Demo

35.2: Implementing Youtube Video And Website Url Content Summarization GEN AI App

36.1: Demo of the End to End Project

36.2: End To End Text to Math Problem Solver Using Google Gemma2 Model Implementation

37.1: Introduction To Huggingface And Langchain Integration

37.2: Langchain And Huggingface Integration Practical Implementation

37.3: End to End Gen AI Project With Langchain And Huggingface

38.1: End To End Project With PDf Query RAG With Langchain And AstraDB

39.1: End To End MultiLanguage Code Assistant Implementation

40.1: Deployment OF Gen AI APP In Streamlit Cloud

40.2: Deployment Of Gen AI App In Huggingface spaces

41.1: Life Cycle Of Gen AI Project In AWS Cloud

41.2: Introduction To AWS Bedrock With Implementation

41.3: Document Q&A RAG With Langchain And Bedrock

41.4: End To End Blog Generation Gen AI Using AWS Lambda And Bedrock

41.5: Deployment Of Huggingface OpenSource LLM Models In AWS Sagemakers With Endpoints

42.1: Building RAG Document Q&A With Nvidia NIM And Langchain

43.1: Youtube Videos To Blog Page Using CrewAI Agents

44.1: Introduction To Hybrid Search

44.2: Reciprocal Rank Fusion In Hybrid Search

44.3: End To End Hybrid Search RAG With Pinecone db And Langchain

45.1: Introduction to Graph DB with Langchain

45.2: What is Knowledge Graph

45.3: Creating Your Neo4j AuraDB Database Instance

45.4: RDBMS VS Graph Database

45.5: Neo4j Property Graph Data Model

45.6: Getting Started With Cypher Query Language

45.7: Intermediate To Advance Cypher Query Languag

46.1: Getting Started-Creating Environment

46.2: Inserting Data In Graph DB With Python And Langchain

46.3: Creating GraphQuery Chain With Langchain

46.4: Prompting Strategies GraphDB With LLM

47.1: What Is Quantization Indepth Intuition

47.2: LORA And QLORA Indepth Mathematical Intuition

47.3: Practical Implementation fine Tuning Custom Data With Google Gemma Model

48.1: End To End Finetuning LLM Models With Lamini AI Cloud

49.1: Introduction To Langgraph

49.2: Creating Chatbots Using Langgraph

49.3: Creating Chatbots With External Tools Workflow With Langgraph

49.4: End to End Multi AI RAG Chatbots Using Langgraph And AstraDB