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Generative AI Syllabus

Fundamentals of AI & ML (Foundation for Generative AI)

● Basics of Artificial Intelligence (AI) and Machine Learning (ML)
● Types of ML: Supervised, Unsupervised, and Reinforcement Learning
● Python code for GenAI (Basics, Data Handling & Preprocessing)
● Basics of Probability, Statistics, and Linear Algebra (for AI models)

Deep Learning – The Backbone of Generative AI

● Introduction to Deep Learning and Neural Networks
● Understanding Neural Networks (CNNs, RNNs, LSTMs, Transformers)
● Optimization Techniques: Backpropagation, Gradient Descent, Adam
Optimizer
● Sequence Models & Attention Mechanism (how models understand
context)
● Transformers & Self-Attention (BERT, GPT).

Generative AI Models & Architectures

● What is Generative AI? How does it work?
● Text Generation Models: GPT-3, GPT-4, LLaMA, Falcon
● Image Generation Models: DALL·E, Stable Diffusion, Midjourney● Audio & Video Generation: MusicLM, Synthesia, RunwayML
● Multimodal AI Models: CLIP, Flamingo (Text + Image AI)

Python Basics

● Variables, Data Types (Strings, Lists, Dicts, Tuples)
● Loops & Conditionals (if-else, for, while)
● Functions (defining and calling functions)
● File Handling (Reading/Writing files – useful for working with datasets)

Data Handling & Preprocessing

● Work with Pandas (for handling structured data)
● Use NumPy (for numerical computations)
● Read and process data from CSV, JSON, Excel, and Databases
● Use Regex (Regular Expressions) for text processing

Introduction to Prompt Engineering

● Definition of Prompt Engineering
● What is Prompt?
● What is Prompt design?

Understanding Large Language Models (LLMs)

● Overview of LLMs

Ram – Certified Generative AI Professional

● Popular LLMs (GPT-3, GPT-4, Claude, Gemini, LlaMa, Grok, Cohere,
Midjourney, FLAN-T5, BERT and more)
● Open-source LLMs (GPT-J, LlaMa, FLAN-T5, BERT, CodeGen, Phi and more)

Understanding Prompt Engineering Strategies

● Instruction-Based Prompting
● Context-Based Prompting
● Example-Based Prompting
● Role-Based Prompting

Understanding the Transformer Architecture

● Define Transformers Architecture
○ Introduction to Attention Mechanism
○ Understand Encoder, Decoder and       Encoder-decoder
○ Key Layers ( 7 Layers)
○ Understanding Query (Q), Key (K),   and Value (V)

Natural Language Processing (NLP) Essentials

● Tokenization (Splitting text into words/sentences)
● Stemming & Lemmatization (Reducing words to root form)
● Vectorization (Converting text into numbers)
● Using NLTK & spaCy (Popular NLP libraries)
● Key Techniques and Tools

Understanding Neural Networks in NLP / LLM

● Types of Neural Networks

○ Convolutional Neural Networks (CNN)
○ Recurrent Neural Networks (RNN)
○ Long Short-Term Memory (LSTM)

Prompt Usage Techniques

● Zero-shot Learning
● One-shot Prompt
● Few-shot Learning
● Fine-tuning

Common Challenges in LLMs

● Hallucination in LLM
● Ambiguity in Prompt Design
● Bias and Fairness

Understanding Tokenization and Its Elements in LLM

● Tokenization Process in LLMs
● Chunking
● Context Window

Advanced Prompt Engineering Models

● Retrieval-Augmented Generation (RAG)
● Chain-of-Thought (COT)
● ReAct (Reasoning and Acting)
● Self-consistency
● Tree-of-Thought Prompting (ToT)

Advanced concepts: Ethical Considerations in Prompting

● Understand Ethical Considerations in LLM
● Prompting Security, Fairness & Bias, Accountability, and Transparency
● AI Security & Prompt Injection Attacks – Jailbreak Prevention

Understand KEY LLM Parameters and its Settings

● Temperature
● Top-K
● Top-P
● Presence Penalty
● Frequency Penalty
● Stop Sequences
● Set Max Tokens

Advanced level Understanding of LLM

● Computational Linguistics used in LLM Training and execution
● Behind the LLM Pre-training Methods
● Understanding Data Pipeline (AI Pipeline) and Preprocessing (Data Cleaning)
● How LLMs are Trained in Quantum Machines

High-level Topics should Understanding

● Autoencoders and Variational Autoencoders (VAEs)
● Generative Adversarial Networks (GANs) – Image & Data Generation

● Vector Databases & Embeddings – FAISS, Pinecone, ChromaDB
● Knowledge Graphs for AI – How structured data improves reasoning

AI Frameworks & Libraries:

● LangChain (For building AI-powered applications)
● PyTorch or TensorFlow (Optional, but useful if you want to fine-tune
models)
● Vector Databases (ChromaDB, Pinecone, FAISS) (For Retrieval-Augmented
Generation – RAG)

Bonus: Hands-on Projects to Build Expertise

● AI Agents & Chatbots (LangChain, AutoGPT, BabyAGI)
● Train a Chatbot using OpenAI API + RAG
● Fine-Tune GPT-4 on a Custom Dataset
● Build an AI-Powered Image Generator (Stable Diffusion, Think Diffusion)
● Create an AI-Driven Marketing Tool for Content Generation
● Develop an AI-Based Code Assistant like GitHub Copilot
● How to use Streamlit to create simple AI-powered web apps
● Deploy AI models to Google Colab or Hugging Face Spaces
● Use FastAPI for serving AI models as APIs

Tools & Platforms We work on :

● Google Cloud
● Google Vertex AI

● Azure Cloud
● Azure AI studio
● AI Agents
● Meta’s LlamaIndex
● OpenAi, Gemini and claude API

Certification & Skill Badges from top Companies:

● Google
● IBM
● Deeplearning AI
● LinkedIn
● Microsoft

Both Free & Paid certifications are available. Paid certifications are self-funded by
individuals.