Complete Generative AI Course: Python to LLM Deployment

Training Type

Select faculty

Select Date

Dur:
Course fee : /-

Download Curriculum

Generative AI (Gen AI)

Course Overview

This comprehensive Generative AI course provides a complete journey from foundational programming concepts to advanced AI model development and deployment. The course covers the entire spectrum of generative artificial intelligence, including Python programming, natural language processing, deep learning, transformer architectures, and modern AI frameworks like LangChain, Hugging Face, and OpenAI APIs.

Students will gain hands-on experience with cutting-edge technologies including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), vector databases, and production-ready AI application deployment. The curriculum is designed to bridge the gap between theoretical understanding and practical implementation, featuring multiple real-world projects and industry-relevant case studies.

Description

This intensive program combines theoretical foundations with practical implementation to master the rapidly evolving field of Generative AI. The course begins with essential Python programming skills and progressively advances through machine learning for NLP, deep learning architectures, and state-of-the-art generative AI technologies.

Key highlights include:

Foundational Programming: Master Python programming, data structures, OOP concepts, and essential libraries
NLP & Machine Learning: Learn tokenization, text preprocessing, vectorization techniques, and word embeddings
Deep Learning Mastery: Understand neural networks, RNNs, LSTMs, and bidirectional architectures
Transformer Architecture: Deep dive into encoders, decoders, attention mechanisms, and transformer models
Modern AI Frameworks: Hands-on experience with Hugging Face, OpenAI APIs, and LangChain ecosystem
Advanced Techniques: Explore RAG systems, vector databases, fine-tuning methods, and LlamaIndex
Production Deployment: Learn to deploy AI applications using Flask and AWS cloud services

The course features over 15 practical projects including chatbots, text summarization, image generation, audio transcription, and custom AI applications.

Course Objectives

Technical Skills

Develop proficiency in Python programming with advanced concepts like OOP, file handling, and exception management
Implement comprehensive NLP preprocessing pipelines including tokenization, stemming, lemmatization, and text vectorization
Build and train neural networks, RNNs, LSTMs, and transformer architectures from scratch
Master attention mechanisms, self-attention, and multi-head attention in transformer models
Design and deploy Large Language Models using Hugging Face transformers and OpenAI APIs
Create sophisticated RAG (Retrieval Augmented Generation) systems with vector databases
Develop conversational AI applications using LangChain and advanced prompt engineering

Practical Applications

Build custom chatbots for websites and specific domains
Create text summarization, translation, and generation applications
Implement image generation systems using DALL-E and text-to-speech applications
Design question-answering systems with document retrieval capabilities
Fine-tune pre-trained models for specific use cases and domains
Deploy production-ready AI applications on cloud platforms

Strategic Understanding

Understand the evolution and architecture of modern LLMs like GPT, BERT, and LLaMA
Evaluate different approaches: fine-tuning vs RAG vs prompt engineering
Design end-to-end AI solutions for real-world business problems

Prerequisites

Essential Requirements
- Basic Mathematics: Understanding of algebra, basic statistics, and logical reasoning
- Computer Literacy: Comfortable with file management, software installation, and basic computer operations
- Learning Mindset: Willingness to engage with technical concepts and problem-solving
Recommended (Not Mandatory)
- Programming Experience: Any prior programming experience (Python, Java, C++, etc.) will be helpful but not required
- Mathematics Background: Basic knowledge of calculus, linear algebra, and probability will accelerate learning
- Data Science Familiarity: Understanding of data analysis concepts, though all necessary concepts will be taught
Technical Setup
- Computer Requirements: Windows, Mac, or Linux system with at least 8GB RAM
- Internet Connection: Stable internet for accessing cloud APIs and downloading datasets
- Software: All required software installations will be guided during the course

Enroll Now

Course Curriculum

Introduction-What We will Learn In This Course

Getting Started With Python
Python Basics-Syntax
Variables In Python
Basics Data Types In Python
Operators In Python

Conditional Statements (if, elif, else)
Loops

Lists and List Comprehension
Tuples
Dictionaries
Sets

Getting Started With Functions
Lambda Function In Python
Map Function In Python
Filter Functions In Python

Import Modules And Packages
Standard Libraries Overview

File Operations With Python
Working with File Paths

Exception Handling With try except else and finally blocks

Classes & Objects
Single And Multiple Inheritance
Polymorphism
Encapsulation
Abstraction

Tokenization
Text Pre-processing

Stemming
Lemmatization
Stopwords

Text Vectorization

Bag Of Words
N Grams
TF-IDF

Word Embeddings

Word2Vec
CBOW
Skip Grams
GloVe

Parts Of Speech Tagging
Named Entity Recognition

Welcome to the module on DL
Introduction to DL
Understanding Deep Learning

What is a Neuron
Activation Functions
Step Function
Linear Function
Sigmoid Function
TanH Function
ReLU Function
Backpropagation vs Forward Pass
Gradient Descent
ANN Intuition
ANN (Hyper Parameter Optimization)
Step By Step Training With ANN

Optimizer
Loss Functions
Finding Optimal Hidden Layers And Hidden Neurons In ANN

RNN Forward Propagation with Time
Simple RNN Backward Propagation
Problems With RNN
End to End Deep Learning Projects with Simple RNN

Why LSTM
LSTM Architecture
Forget Gate In LSTM
Input Gate And Candidate Memory In LSTM
Output Gate In LSTM
Training Process In LSTM
Variants Of LSTM
GRU RNN Indepth Intuition
LSTM and GRU End to End Deep Learning Project

Bidirectional RNN

Why To Use It?
Advantages & disadvantages
Applications

Introduction to Encoders
Encoder Architecture
Introduction to BERT
BERT Configurations
BERT Fine Tuning
BERT Pre Training (Masked LM)
Input Embeddings BERT
RoBERTa
DistilBERT
AlBERT

Introduction to Decoders
Decoder Architecture
GPT Architecture
GPT (Masked Multi Head Attention)
GPT Training

Encoder and Decoder
Indepth Intuition oF Encoder & Decoder
Sequence to Sequence Architecture
Problems With Encoder and Decoder

Seq2Seq Networks
Attention Mechanism Architecture

What and Why To Use Transformers
Understanding The basic Architecture of Encoder
Self Attention Layer Working
Multi Head Attention
Feed Forward Neural Network With Multi Head Attention
Positional Encoding
Layer Normalization
Layer Normalization Examples
Complete Encoder Transformer Architecture
Decoder-Plan Of Action
Decoder-Masked Multi Head Attention
Encoder and Decoder Multi Head Attention
Decoder Final Linear And Softmax Layer

What is Generative AI- AI Vs ML Vs DL Vs Generative AI
How Open AI ChatGPt or LLama3 LLM Models are trained
Evolution of LLM Models
All LLM Models Analysis

Data Preprocessing

Cleaning
embeddings

End to end Generative AI Pipeline

Introduction to Large Language Models & its architecture
In depth intuition of transformer – Attention all your need paper
How ChatGPT is trained.

Introduction of hugging face
Hands on Hugging Face – Transformers, HF Pipeline, Datasets, LLMs
Data processing, tokenizing and feature extraction with hugging face
Fine – Tuning using a pretrain models
Hugging face API key generation
Project: Text summrization with hugging face
Project: Text to Image generation with LLM with hugging face
Project: Text to speech generation with LLM with hugging face
Huggingface Platform and its API

Introduction to OpenAI
What is OpenAI API and how to generate OpenAI API key?
Local Environment Setup
Hands on OpenAI – Chat completion API and Completion API
Function Calling in OpenAI
Project: Fine-tuning of GPT-3 model for text classification
Project: Audio Transcript Translation with Whisper
Project: Image generation with DALL-E

Vector Databases
Vector Index vs Vector Database
How Vector db works
Vector Database (Practicals)

Complete Langchain Ecosystem
Creating Virtual Environment
Getting Started With Langchain And OpenAI

Introduction & Installation and setup of langchain
Prompt Templates in Langchain
Chains in Langchain
Langchain Agents and Tools
Memory in Langchain
Documents Loader in Langchain
Multi-Dataframe Agents in Langchain
How to use Hugging face Open Source LLM with Langchain
Project: Interview Questions Creator Application
Project: Custom Website Chatbot

Introduction To Basic Components And Modules in Langchain
Data Ingestion With Documents Loaders
Text Splitting Techniques

Recursive Character Text Splitter
Character Text splitter
HTML Header Text Splitter
Recursive Json Splitter

OpenAI Embedding

Ollama Embeddings
Huggingface Embeddings

Introduction to open source LLMs – Llama
How to use open source LLMs with Langchain
Custom Website Chatbot using Open source LLMs
Open Source LLMs - Falcon

Introduction & Importance of RAG
RAG Practical demo
RAG Vs Fine-tuning
Build a Q&A App with RAG using Gemini Pro and Langchain
Retrieval Augmented Generation (RAG)

What is fine tuning?
Parameter Efficient Fine – Tuning

LoRA
OLoRA
Meta Llama 2 on Custom Data

Introduction to LlamaIndex & end to tend Demo
Project: Financial Stock Analysis using LlamaIndex

How to Deploy Generative AI Application

Flask
AWS

APPLY NOW !

Who can learn this course

Complete Beginners

Individuals with no programming experience who want to enter the AI field
Career changers looking to transition into artificial intelligence and machine learning
Students seeking comprehensive foundation in modern AI technologies

Technology Professionals