Generative AI Specialist (Batch-01)

Wishlist Share
Share Course
Page Link
Share On Social Media

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 – Introduction of Generative AI | AI Future Prospects | Career Opportunities | Training Modules | Instructor Introduction | QnA Session

  • Demo Session
    02:18:42

Class 01 – Basics of Python (Punctuations, Mathematical Operators, Brackets and Craces, Logical and Comparison) | Syntax and Semantics | Conditional Statement | Python Variables (Variables, Declaring and Assigning Variables, Naming Conventions, Types) | Practical Examples and Common Errors

Class 02 – Data Types (Integers, Floating, Point Numbers, Strings, Booleans) | Advanced Data Types (Lists, Tuples, Sets, Dictionaries) | Operators (Arithmetic, Comparison, Logical) | Conditional Statements (IF, ELIF, ELSE) | Practical Example – Employees Bonus Calculator

Class 03 – Loops (For Loop and While Loop) | Iterating over Range | Iterating over String | Loop Control Statements (Break, Continue, Pass) | Nested Loops | Data Structure (List, Tuples, Sets, Dictionaries) | List Comprehension

Class 04 – List Practical Examples (Managing an Inventory, Collecting Feedback) | Data Structure (Tuples) – Creating, Assessing and Operations | Mutable vs Immutable Tuples | Methods | Packing and Unpacking Tuples | Nested Tuples

Class 05 – Data Structure (Dictionaries) – Creating, Assessing and Modifying | Methods | Looping over Dictionaries | Nested Dictionaries | Dictionaries Comprehensions | Functions (Introduction to Functions, Defining Functions, Calling Functions, Parameters, Variable-Length Arguments, Return Statements)

Class 06 –

Class 07 – Python Libraries Overview (Array, Math, OS, Random, Shutil, JSON, CSV, Datetime) | File Operations (Read and Write Files) | Working with File Paths | Object-Oriented programming (OOP)

Class 08 – Object-Oriented programming (OOP) Basic | Inheritance in Python (Single and Multiple) | Polymorphism | Polymorphism with Abstract Base Classes

Class 09 – Polymorphism with Abstract Base Classes (Interfaces) | Encapsulation (Protected Method)

Class 10 – Encapsulation with Getter and Setter (Public, Protected, Private Variables or Access Modifiers)

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

Class 12 –

Class 13 –

Class 14 – Prediction and Deployment | Churn Modelling | ANN Implementation | Label Encoder Gender | One-Hot Encoder

Class 15 – Salary Regression | Encode Categorical Variables | One-Hot Encode | ANN Regression | Regression Model | Set Up TensorBoard

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

Class 17 – Input Gate and Candidate Emory | Output Gate in LSTM | Training Process in LSTM RNN | GRU RNN Intuition

Class 18 – Understanding LSTM RNN – Forget Gate | Input Gate | Candidate Memory | Output Gate | Explaining the Role of Each Gate in Controlling Information Flow | How Forget Gate Decides What to Discard | Input Gate Regulates Incoming Information | Candidate Memory Stores New Information | Output Gate Determines Final Output of the Cell

Class 19 – Understanding GRU RNN | Initiated Next Word Prediction Project | Exploring Gated Recurrent Unit Architecture and Functionality | Comparing GRU with LSTM for Sequence Modeling | Starting Hands-On Project for Predicting Next Word Using GRU Model

Class 20 – End-to-End Development of Next Word Prediction Project | Data Collection | Data Preparation and Preprocessing | Model Building | Model Training | Model Evaluation | Deployment | Complete Workflow from Raw Data to Deployed Model | Implementing LSTM and GRU for Sequence Prediction

Class 21 – Introduction to LangChain and GenAI (Theoretical Class) | Overview of LangChain Framework | Key Concepts of Generative AI | Use Cases and Applications | Importance in Modern AI Development | Basic Architecture and Components

Class 22 – Introduction to Generative AI and LLM (Large Language Model) | LangChain for Generative AI | OpenAI and LangChain Project (LangSmith, LangServe, OpenAI)

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

Class 24 – Building LLM Application using LCEL: Setting .ENV | API KEY Creation | Using Human and System Messages Structuring Model Inputs | Extracted Content using an Output Parse | Using LCEL to Chain Multiple Component | Invoking the Chain | Prompt Template to Simplify and Structure Input Messages | LANGSERVE | Created and Tested API endpoints with FASTAPI

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 the Chat Conversation History using LangChain (Managing Conversation History, Trim Messages Helper Method, Token Limit, Trimming Strategies, Conversation Trimming) | Working with Vectorstore and Retriever (Overview Vector Store and Retrievers in LangChain, Creating Documents and Storing them as Vectors with a Chroma Vector, Store Query – The Vector Store using Similarity Search, Converting Vector Store into a Retriever for Easier Integration, Combined Retriever with a Chain (RAG – Retrieval Augmented Generation)

Class 28 – Project: Developed End to End Q&A Chatbot Gen AI Application with Info (using Groq/ OpenAI/ Ollama Models)

Class 29 – Project: Build End to End Q&A Chatbot Gen AI App using Olama | Initiated Project: RAG Document/ PDF Reader and Q&A 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)