Generative AI Specialist (Batch-03)

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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

  • Generative AI (Batch-03) – Introductory Class
    02:02:47

Class 01 – Python (Getting Started & Setup) | Variables & Data Types | Print Statements | Comments

Class 02 – Python Basics (Data Types & Control Flow) | type() & Type Conversion | User Input | Conditional Statements

Class 03 – Python (Loops & Functions) | For & While Loops | Applying Functions on Data Types

Class 04 – Advanced Python Data Structures | Lists & Methods | Dictionaries

Class 05 – Tuples | Sets | Their Properties and Usage | Common Operations and Built-in Functions

Class 06 – Functions in Python (Definition, Parameters, Return) | Introduction to Git & GitHub | Version Control Basics | Creating Repositories | Pushing Code to GitHub

Class 07 – Object-Oriented Programming in Python | Classes & Objects | Inheritance

Class 08 – Python (Intermediate Concepts) | Inheritance | Virtual Environments | spaCy Setup | Text File Handling

Class 09 – Polymorphism & Method Overriding | Abstraction in OOP | Reading PDFs with Python (PyPDF/fitz) | Intro to NLP with spaCy (Tokenization, Basics)

Class 10 – Access Modifiers in Python | Public, Private & Protected Variables/Methods | Regular Expressions (Pattern Matching, Search & Replace)

Class 11 – Abstraction in Python | Introduction to Open-Source LLMs | Overview of Ollama & LM Studio | Using Open-Source LLMs via Python

Class 12 – Introduction to LM Studio & Ollama | Basics of LangChain Components | Prompts | LLMs | Output Parsers

Class 13 – Introduction to Data Analysis | Basics of NumPy & Pandas | Building Interactive UI with Streamlit

Class 14 – Project 1: Enterprise Chatbot | Project 2: Document Summarizer (Concepts & Initiation)

Class 15 – RAG Theory | Project 3: Q&A on Documents (Part 1 – Embeddings)

Class 16 – Project 3: Q&A on Documents (Part 2 – Retriever and Chain)

Class 17 – Tokenization & Named Entity Recognition (NER) using LangChain and LLMs | Text breakdown & entity extraction | Practical implementation

Class 18 – Language Translator App | LangChain Tools | Pandas DataFrame Agent

Class 19 – Introduction to Graph Databases | Neo4j Basics | Cypher Query Language

Class 20 – Tool Calling with LangChain | ReAct Agent | ToolNode & Manual Integration

Class 21 – Introduction to Agentic AI Frameworks | LangGraph Basics | Projects: Chatbots with & without Tools

Class 22 – Static Methods | Class Methods | Iterators | Generators | Threading

Class 23 – Project: Reflection Critic Architecture for PDF Summarization

Class 24 – Neo4j Desktop Setup & Python Connection for Graph RAG

Class 25 – Multimodal LLM | ChatModels in LangChain

Class 26 – Project: Knowledge Graph Creation | Collection of Resumes | Graph RAG

Class 27 – Project: Graph RAG | Collection of Resumes | Multiple Document Classes