Generative AI Specialist (Batch-01)

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What Will You Learn?

  • Master the creation of advanced generative AI applications using the Langchain framework and Huggingface's cutting-edge models.
  • Understand the architecture and design patterns for building robust and scalable generative AI systems.
  • Gain practical experience in deploying generative AI models across various environments, including cloud platforms and on-premise servers.
  • Explore deployment strategies that ensure scalability, reliability, and optimal performance of AI applications.
  • Develop Retrieval-Augmented Generation (RAG) pipelines to boost the accuracy and efficiency of generative models by integrating retrieval mechanisms.
  • Seamlessly incorporate Huggingface's pre-trained models into Langchain applications to leverage their powerful NLP capabilities.
  • Customize and fine-tune Huggingface models to meet specific application needs and use cases.
  • Engage in real-world projects demonstrating Generative AI applications in domains such as chatbots, content generation, and data augmentation.

Course Content

Introductory Class – Overview of Generative AI | Future Scope and Career Opportunities | Course Structure and Training Modules | Instructor Introduction | Interactive Q&A Session

  • Demo Session
    02:18:42

Class 01 – Basics of Python | Operators, Brackets, Syntax, and Semantics | Conditional Statements | Variables and Types | Common Errors and Examples

Class 02 – Data Types | Advanced Data Types | Operators (Arithmetic, Comparison, Logical) | Conditional Statements | Employee Bonus Calculator

Class 03 – Loops (for, while) | Loop Control (break, continue, pass) | Nested Loops | Iterating over Range & Strings | Data Structures Overview | List Comprehension

Class 04 – List practical examples (inventory, feedback collection) | Tuples – creation, access, operations | Mutable vs immutable | Tuple methods | Packing & unpacking | Nested tuples

Class 05 – Dictionaries – creation, access, modification | Dictionary methods | Looping and nested dictionaries | Dictionary comprehensions | Functions – intro, defining, calling, parameters, *args/**kwargs, return statements

Class 06 –

Class 07 – Python libraries overview (array, math, os, random, shutil, json, csv, datetime) | File operations – reading, writing, paths | Introduction to OOP

Class 08 – Object-oriented programming basics | Inheritance (single and multiple) | Polymorphism | Abstract base classes and polymorphism

Class 09 – Abstract base classes and interfaces | Encapsulation using protected methods

Class 10 – Encapsulation with getter and setter methods | Understanding public, protected, and private access modifiers

Class 11 – Theory: One-Hot Encoding | Bag of Words (BOW) | TF-IDF | Word2Vec | ANN (Artificial Neural Network) | CNN (Convolutional Neural Network) | Classification

Class 12 –

Class 13 –

Class 14 – Churn Prediction and Model Deployment | ANN Implementation | Label Encoding (Gender) | One-Hot Encoding

Class 15 – Salary Prediction Using Regression | Categorical Variable Encoding | One-Hot Encoding | ANN for Regression | Model Evaluation | TensorBoard Setup

Class 16 – LSTM RNN | LSTM Architecture | Forget Gate in LSTM | Understanding LSTM Networks

Class 17 – LSTM Input & Output Gates | Candidate Memory | LSTM Training Process | GRU RNN Overview

Class 18 – LSTM Gates Explained: Forget, Input, Candidate Memory, Output | Role of Each Gate in Managing Information Flow

Class 19 – GRU RNN Overview | GRU vs LSTM Comparison | Initiated Next Word Prediction Project Using GRU

Class 20 – End-to-End Next Word Prediction Project | Data Collection to Deployment | Preprocessing | Model Building and Training | Evaluation | Implemented LSTM and GRU for Sequence Prediction

Class 21 – Introduction to LangChain and Generative AI | LangChain Framework Overview | Key Concepts and Use Cases | Importance in Modern AI | Basic Architecture and Components

Class 22 – Introduction to Generative AI and Large Language Models (LLMs) | Using LangChain for GenAI | Overview of OpenAI Tools in LangChain Projects (LangSmith, LangServe, OpenAI Integration)

Class 23 – Bidirectional RNN | Encoder Decoder | Attention Mechanism | Introduction of Transformers

Class 24 – Built LLM App with LCEL | Set .ENV & API Keys | Structured Prompts | Parsed Outputs | Chained Components | Used LANGSERVE & FASTAPI for API Endpoints

Class 25 – Project: Building a Chatbot with Message History using LangChain

Class 26 – Started building a ChatBot | Creating Message History | Working on Prompt Templates

Class 27 – Managing Chat History in LangChain (Trimming Methods, Token Limits, Conversation Strategies) | Vectorstore & Retriever (Chroma Vectors, Similarity Search, Retriever Integration, RAG Implementation)

Class 28 – Project: Built End-to-End GenAI Q&A Chatbot Using Groq, OpenAI, and Ollama Models

Class 29 – Project: Developed Q&A Chatbot GenAI App Using Ollama | Initiated RAG-Based Document/PDF Reader and Q&A System with Groq API and LLaMA3

Class 30 – Project: Conversational Q&A Chatbot- Chat With PDF Along With Chat History

Class 31 – Project: Search Engine with Tools and Agents (Phase 01)

Class 32 – Project: Search Engine with Tools and Agents (Phase 02)

Class 33 – Generative AI Project: Chat with SQL DB with LangChain SQL Toolkit and AgentType (Phase 01)

Class 34 – Generative AI Project: Chat with SQL DB with LangChain SQL Toolkit and AgentType (Phase 02)