Generative AI

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Generative AI

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Learning Path

Python & Data Structures

Introduction to Python

Installation and Setup:
Installing Python and setting up a development environment (IDEs like PyCharm, VSCode, Jupyter Notebooks)
Syntax and Basic Constructs:
Variables and data types (integers, floats, strings, booleans)
Basic input and output
Comments and documentation
Control Structures
Conditional Statements:
if, else
Loops:
for, while
Loop control statements (break, continue, pass)
Functions
Defining Functions:
Parameters and return values
Scope and Lifetime:
Local and global variables
Lambda Functions:
Anonymous functions

Data Structures & Algorithms

Core Data Structures
Lists:
Creating, accessing, modifying, and iterating over lists
List comprehensions
Tuples:
Creating and using tuples
Unpacking tuples
Sets:
Creating and using sets
Set operations (union, intersection, difference)
Dictionaries:
Creating and using dictionaries
Dictionary methods and comprehensions

File Handling and Data Processing

File Operations
Reading and Writing Files:
Opening, reading, writing, and closing files
Working with different file modes (r, w, a, rb, wb)
Working with CSV and JSON:
Reading from and writing to CSV and JSON files using csv and json modules

Object-Oriented Programming

OOPs Basics
Classes and Objects:
Defining classes and creating objects
Instance variables and methods
Class Variables and Methods:
Using class variables and class methods
Inheritance:
Single and multiple inheritance
Polymorphism and Encapsulation:
Method overriding
Private variables and name mangling

Exception Handling and Debugging

Exception Types:
Common exceptions (ValueError, TypeError, ,)
Try, Except Blocks:
Using try, except, else, and finally

Working with Libraries

Scientific Computing
NumPy:
Arrays and matrix operations
Pandas:
DataFrames for data manipulation
Reading and writing data (CSV, Excel)
Data Visualization
Matplotlib:
Plotting graphs and charts
Seaborn:
Statistical data visualization

Generative AI

Introduction

GenAI and It’s Industry Applications
Introduction to Generative AI
AI vs ML vs DL vs NLP vs Generative AI
Generative AI principles
What is the role of ML in Gen-AI
Different ML techniques (Supervised, Unsupervised, Semisupervised & Reinforcement Learning)
Applications in various domains
Ethical considerations

NLP & Deep Learning

NLP essentials
Basic NLP tasks
Different text classification approaches
Frequency based – Bag of words,TF-IDF, N-gram.
Distribution Models – CBOW, Skipgram (Traditional approaches) and word2vec, Glove.
Deep learning techniques – CNNs, RNNs, LSTMs, GRU and Transformers

Generative AI Models

Auto encodes
VAE’s and applications
GAN’s and it’s applications
Different types of GAN’s and applications

Language Models & Transformer Models

Different types of Language models
Applications of Language models
Transformers and its architecture
BERT, RoBERTa, GPT variations
Applications of transformer models

Prompt Engineering

What is Prompt Engineering
What are the different principles of Prompt Engineering
Types of Different Prompt Engineering Techniques
How to Craft effective prompts to the LLMs
Priming Prompt
Prompt Decomposition

Large Language Models

Generative AI lifecycle
What is RLHF
LLM pre-training and scaling
Different Fine-Tuning techniques

Different Chunk Metrics

What is Chunking
What is the use of chunking the document
What are the traditional effective chunking techniques
What are the problems and limitations with traditional chunking techniques?
How to overcome the limitations of Traditional chunking
Advanced Chunking Techniques:
1. Character Splitting
2. Recursive Character Splitting
3. Document based Chunking
4. Semantic Chunking
5. Agentic Chunking

RAG and Advanced RA with Langchain

What is RAG
What are the main components of RAG
High level architecture of RAG
How to Build RAG using external data sources
Advanced RAG

Langchain for LLMs

What is Langchain
What are the core concepts of Langchain
Components of Langchain
How to use Langchain agents

Vector Databases

LlamaIndex
What are Vector Databases
Why do we prefer Vector Databases over Traditional Databases
Different Types of Vector Databases: OpenSource and Close Source
OpenSource: Chroma DB, Weaviate,Faiss,Qdrant
Close-Source Vector Databases:Pinecone,ArangoDB,Cloud-Based Solutions

Finetuning LLMs

Supervised Finetuning
Repurposing-Feature Extraction
Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA

LLMs Evaluation

Text based LLMs
Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT
Human Evaluation: Coherence, Factuality, Originality, Engagement

Image based LLMs

Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception Distance), IS (Inception Score), Perceptual Quality Metrics, Diversity Metrics.
Human Evaluation: Photorealism, Style, Creativity, Cohesiveness

Audio generation LLMs:

Automatic Evaluation: FAD (Frechet Audio Distance), IS (Inception Score), Perceptual Quality Metrics – PAQM, PAQM – SNR (Signal-toNoise Ratio), PAQM – PESQ (Perceptual Evaluation of Speech Quality)
Human Evaluation: Perceptual Quality – PQ, PQ- Naturalness, PQFidelity, PQ- Musicality, Task Specific Evaluation.

Video Generation LLMs:

Automatic Evaluation: FVD (Frechet Video Distance), Inception Score(IS), Perceptual Quality Metrics, Motion Based Metrics – Optical Flow Error, Content-Specific Metrics.
Human Evaluation: Visual Quality, Temporal Coherence, Content Fidelity

LLMops

Model Deployment and Management
Scalability and Performance Optimization
Security and Privacy
Monitoring and Logging
Cost Optimization
Model Interpretability and Explainability.

LLM's on Cloud

Amazon Bedrock, Azure OpenAI

Langsmith

What is langsmiths?
Applications and use-cases

Agentic ai & MCP

What is agentic ai
Building single agent
Multi agents by using MCP

Different AI Tools

Open AI
Hugging face
PyTorch
tensorflow
lang chai & lang graph
ChatGPT
Gemini
Copilot

Frequently Asked Questions

What is the Generative Artificial Intelligence?

Generative AI is a type of artificial intelligence that can create new content, such as text, images, music, video, code, or even synthetic data — by learning from existing data. Instead of just analyzing or categorizing data like traditional AI, Generative AI can generate new and original material that resembles human-created content.

What are the key topics covered in this course?

The program covers:

Python & Data Structures :

Introduction to Python
Data Structures & Algorithms
File Handling and Data Processing
Object-Oriented Programming

GENERATIVE AI:

Introduction
NLP & Deep Learning
Generative AI Models
Language Models & Transformer Models
Prompt Engineering
Large Language Models
Different Chunk Metrics
Langchain for LLMs
Vector Databases
LLMs Evaluation
Image based LLMs
Audio generation LLMs
Video Generation LLMs
LLM’s on Cloud

Is this course certified?

Yes, upon successful completion, participants receive a Generative AI (Artificial Intelligence) Certificate from SGD Professional IT Solutions, which is recognized by the AICTE industry.

What are the prerequisites to join this course?

There are no strict prerequisites, You don’t need to be an AI expert to start learning Generative AI, but having some foundational knowledge will help you understand concepts more effectively. Here’s a breakdown of recommended prerequisites depending on the course level (beginner to advanced).

What career opportunities are available after completing this course?

Gen Ai Developer
Lead Data scientist
Gen Ai architects
NLP engineer
ML Engineer
AI Content Creator
Prompt Engineer
AI Product Manager

Will I get placement assistance after the course?

Yes, SGD Professional IT Solutions provides 100% Guarantee Placement Support, including resume-building assistance, interview preparation, and job referrals with partner companies.

Like the curriculum? Enroll Now

Structure your learning and get a certificate to prove it.

SGD Professional Training Features

Get Trained By Real Time Industry Experts

The Participants will get an opportunity to get trained from real time industry experts in specific domains having more than 10 yrs of experience.

24/7 Access to our e-learning platform

The participants will get 24/7 access to our creative and vast extensively designed study materials and videos along with countless no.of information regarding career opportunities as per the industry standards

24/7 Technical support for participants

SGD Professional IT Solutions is committed to provide 24/7 quality technical support services to all our participants with no compromise.

Lifetime free 100% job assistance and on job support

Our dedicated HR consultants work 24/7 to maintain healthy relationship with various top job portal companies and job consulting services to place our participants in more than 500+ startup companies and MNC’s.

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Generative AI

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Our students were hired by :

Like the curriculum? Enroll Now

Get Trained By Real Time Industry Experts

24/7 Access to our e-learning platform

24/7 Technical support for participants

Lifetime free 100% job assistance and on job support

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