Generative AI course for Beginner with hands-on Project

Generative AI is reshaping industries, creating new opportunities in AI development, automation, and intelligent applications.

If you want to break into AI or enhance your technical skills, this Generative AI course for beginners with hands-on projects is your stepping stone to becoming a Generative AI Engineer.

Learn to train, fine-tune, and deploy AI models that can generate text, assist in decision-making, and automate workflows—all with real-world projects that solidify your skills.

Large Language Models (LLMs) like BERT, GPT-4, Copilet, Mistral, Grok, Llama and Gemini are transforming industries.

Why Become a Certified Generative AI Engineer?

With AI transforming industries at an unprecedented pace, companies are seeking Generative AI Engineers who can develop AI-powered applications.

Whether you want to boost your career, work on AI projects, or stay ahead in tech, mastering Generative AI will give you a competitive edge in the job market.

Start your Generative AI journey today! Learn from scratch, build real projects, and become a Generative AI Engineer.

Who Is This Course For?

This Generative AI course for beginners is designed for:

• Anyone who wants to learn Generative AI step by step and apply it in real-world projects in the AI era.
• Students, Developers, web designers, UI/UX developers, healthcare, finance, medical, Educational industry working professionals looking to transition into Generative AI roles. 
• AI enthusiasts eager to build practical AI solutions with hands-on experience

Fundamentals of AI & ML (Foundation for GenAI)

• 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)
• Real-world applications of AI in different industries

Deep Learning – The Backbone of GenAI

• 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)

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
• Popular LLMs (GPT-3, GPT-4, Claude, Gemini, LlaMa, Grok, Cohere, Midjourney, 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)

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

Understand LLMs Pre-training and Tuning

• 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

Why Take This Generative AI Course?

• Beginner-Friendly, Hands-On Learning – No prior AI experience required! Start from scratch and build practical AI models.
• Industry-Relevant Skills – Work with LLMs, RAG, AI Agents, and Cloud Deployments just like AI professionals.
• Master Prompt Engineering – Learn how to craft powerful prompts to get better AI-generated responses.
• Fine-Tune AI Models – Adapt GPT, Google Gemini, and Meta LLaMA for real-world applications.
• Deploy AI in the Cloud – Train and host your models on GCP, and Azure to build scalable AI solutions.

By the end of this course, you’ll have in-demand Generative AI skills and a portfolio of real AI projects that showcase your expertise.

AI Frameworks & Libraries:

• LangChain / LlamaIndex (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

• Build and Train AI Agent using Gemini / OpenAI API + RAG
• Work with AI-Powered Image Generator (Stable Diffusion, Think Diffusion)
• How to use Streamlit to create simple AI-powered web apps
• Deploy AI models to Google Colab or Hugging Face Spaces
• Use FastAPIfor 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