L47- AWS Services Deep Dive | Lambda Serverless, API Gateway, Bedrock Model Catalog & Playgroun

L46- AWS Cloud Intro in Hindi | Bedrock, S3, EC2, Regions, Root vs IAM Users & GoDaddy Domain

L45- Agentic RAG | RAG as a Tool, Multi-Tool Agent, Vector DB & AWS Preview

L43- MCP Explained in Hindi | Model Context Protocol, Client-Server, stdio vs HTTP

L44- Real APIs as MCP Tools | OpenWeather + GNews Integration, File System MCP

L42- Tools in Agentic AI | @tool Decorator, Docstring, Schema, Multi-Tool Calling

L41 -LangGraph Hands-On | State, Nodes, Edges, @tool Decorator & Real Bank Agent with Code

L40- Agentic AI | What are AI Agents, Tools, Workflows, LangGraph & Frameworks

L39-2- Word2Vec, TF-IDF & BERT Embeddings in Hindi | CBOW, Skip-gram, GloVe & Contextual Embeddings

L39-1— Classical NLP | Corpus, Vocab, Tokenization, Bag of Words, Stemming & Lemmatization

L38- Fine Tuning | LoRA Rank, Epochs, Batch Size, Learning Rate & Hugging Face Upload

L36 — Fine Tuning & Quantization | LoRA, Memory Math, Dataset Prep & RAG vs Fine Tuning

L37 — Fine Tuning LLM | Kaggle GPU, Unsloth, LoRA Matrix Math & QLoRA Hands-On

L35 — Decoder Architecture Explained | Masked Attention, Cross Attention

L34 -Transformer Architecture | Tokenization, Embeddings, Self-Attention & QKV

L33-RAG Evaluation Metrics - Measure & Improve System Accuracy

L32-Complete RAG Chatbot - Build Web App with Document Q&A & Memory

L31B-Complete Reterival Pipeline

L-31A: Hybrid Search & Reranking - Boost RAG Accuracy with Advanced Retrieval

L30-Vector Databases & Retrieval

L29-Embeddings Explained - Convert Text to Vectors for RAG & Semantic Search

L28-Text Chunking & Splitters - Master Document Segmentation for RAG

L27-Document Loaders & Docling - Extract Data from PDFs, Web & Files

L26-RAG System Fundamentals - Retrieval, Augmentation & Generation Explained

L25-Structured Output with LLMs | Extract Data from Images & Documents

L24-Streamlit Tutorial - Create Interactive Python Web Apps with UI Components

L23-Master Prompt Engineering - Zero Shot, One Shot & Few Shot Techniques

L22-Message Placeholders & Conversation History - Building Stateful Chatbots with LangChain

L21-LangChain ChatPromptTemplate - Message Templates, Placeholders & Chains

L20-LLM Model Parameters Guide - Temperature, Max Tokens, Top P & Stop Sequences

Lecture 40: AWS Bedrock & RAG Systems | Complete Gen AI Setup Guide (Part1)

L19-LangChain Setup & OpenAI API Integration | Generative AI Hindi

L18 - Generative AI API Setup: Open AI, Claude, Gemini & Pricing

L17 - Generative AI: Understanding Model Parameters, Tokens & Cloud Computing

LECTURE 38 - Build AI Pandas Agents | Natural Language CSV Analysis with LLM

Lecture 37 - Pandas for Data Analysis | Building AI Pandas Agent with LLM

OpenAI API Key Setup - Complete Purchase & Integration Guide | Hindi Tutorial 🔑🤖

L16-Advanced Python OOP - Magic Methods, Decorators & Operator Overloading | Hindi 🐍✨

L15- Python OOP - Classes, Encapsulation & Inheritance | Complete Tutorial in Hindi 🐍

L14-Classes & Objects in Python | OOP Fundamentals | Constructor & Methods

Lecture 36: Build SQL Agents with LangChain | Natural Language to SQL | Human-in-the-Loop

Lecture 35: SQL Databases & SQL Agents | Structured vs Unstructured Data | Database Fundamentals

L13 - Decorators, Iterators & Generators in Python

Lecture 34: Long-Term, Episodic & Semantic Memory in Chatbots - Building Personalized AI Agents

Lecture 33: Chatbot Memory & Context Management - Short-Term Memory, Message Trimming & LangChain

L12-Lambda Functions, Map & Filter in Python - Functional Programming Mastery in Hindi

L11-List Comprehension & Python Functions - args, Default Arguments & More in Hindi

L10-Python Loops - For Loop, While Loop, Break, Continue & Nested Loops Explained in Hindi

Lecture 32 : Short-Term Memory in Chatbots: Complete Implementation with Langchain 1.1 (Middleware)

Lecture 31-Build AI Chatbot from Scratch: Complete Guide with Memory, Langchain 1.1

L9-2- Model Router: Intelligent LLM Routing for Cost & Speed Optimization

L9-1-LLM AS JUDGE -Compare AI Models Automatically | Evaluating GPT vs Gemini with AI

Lecture 15 -Text Chunking Explained: Character vs Recursive Splitting | RAG Series Tutorial

Lecture 17 - Complete RAG Pipeline: From Document to Vector Store | End-to-End Implementation

Lecture 16 - Vector Embeddings Explained: How to Choose the Best Embedding Model | RAG Series

Lecture 14: Document Loaders & Text Chunking in LangChain | PDF Processing & Splitting Strategies

Lecture 8 - Prompt Engineering Techniques (Zero-shot, One-shot, Few-shot, CoT & ToT) with LangChain

Lecture 7 - Prompt Engineering Basics with LangChain | Clear Prompts, Context & Best Practices

Lecture 15 - Error handling and module

Lecture 14- Encapsulation and Abstraction