Generative AI Training Course

SKU: 8415
9 Lesson
|
35 Hours
5 (5 reviews)
Generative AI (Gen AI) is a branch of Artificial Intelligence that enables machines to generate new and human-like content, such as text, images, code, audio, and video, by learning from large datasets. igmGuru's Generative AI Training helps you develop these skills through a structured curriculum developed by experienced industry professionals. The course combines strong fundamentals, hands-on projects, prompt engineering, and LLM integration.

Generative AI Training Certification Overview

igmGuru’s Gen AI training course is designed for both beginners and working professionals. It starts with the basics of Artificial Intelligence and Machine Learning, then gradually moves toward advanced concepts and real-world deployment strategies.

The course outline covers AI foundations, Large Language Models, prompt engineering, AI-driven content automation, multimodal systems, Retrieval-Augmented Generation (RAG), model fine-tuning, AI agents, API integration, ethics, and enterprise deployment.

What Will You Learn in This Generative AI Course

Beginner Level

Intermediate Level

  • Advanced Prompt Engineering (system prompts, structured outputs, few-shot and zero-shot prompting).
  • Understanding LLM parameters such as temperature, tokens, and top_p.
  • AI API integration fundamentals and building AI chatbots.
  • Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
  • AI image generation basics and AI-powered marketing automation.
  • Code generation, workflow automation, and AI-driven data analysis.

Advanced Level

  • Fine-tuning Large Language Models and building AI Agents.
  • Multi-agent workflows, memory management, and LLM orchestration frameworks including LangChain fundamentals.
  • Vector database integration, prompt chaining, and reasoning frameworks.
  • Enterprise AI architecture design and production-ready AI application development.
  • AI deployment best practices, security, governance, and responsible AI frameworks.
  • Performance optimization and cost optimization strategies for AI APIs.

Who Should Enroll in This Gen AI Training?

  • Beginners interested in AI
  • Software developers
  • Data analysts and data engineers
  • Business analysts
  • Product managers
  • Marketing professionals
  • Content creators
  • Startup founders
  • Automation specialists
  • IT professionals
  • Students aspiring to enter AI careers
  • Entrepreneurs wanting AI integration
  • Corporate teams adopting AI transformation

Prerequisites of Gen AI Training

  • No prior AI experience required.
  • Basic computer knowledge.
  • Familiarity with internet tools.
  • Basic understanding of programming (helpful but not mandatory).
  • Logical thinking and curiosity to learn.

Career Opportunities After Gen AI Training

After completing this Generative AI Training, you can pursue roles such as:

  • Generative AI Engineer
  • Prompt Engineer
  • AI Application Developer
  • LLM Integration Specialist
  • AI Automation Engineer
  • AI Product Specialist
  • AI Consultant
  • Conversational AI Developer
  • AI Research Assistant
  • AI Content Strategist

Why Choose igmGuru’s Gen AI Course?

  • Step-by-step learning path
  • Live mentor support
  • Real-world projects
  • Career-focused training
  • Professional certification
  • 100% placement assistance

Industries That Use Gen AI-Based Applications

  • Technology and Software Companies
  • Financial Services and FinTech
  • Healthcare and Pharmaceuticals
  • E-commerce and Retail
  • Media and Entertainment
  • Education Technology
  • Consulting and Professional Services
  • Startups and Innovation Labs

Generative Ai Training Key Features

Generative AI Course Syllabus

1. Introduction to Generative AI
2. Generative AI Applications
3. Understanding Probability and Statistics in Generative AI
4. Introduction to Generative Models
5. Deep Learning for Generative Models
6. Introduction to Generative Adversarial Networks (GANs)
7. Autoencoders
8. Transformers and Attention Mechanisms - "Attention is all you need".
1. Introduction to Large Language Models (LLMs)
2. Architecture of Large Language Models
3. Text AI LLMs (GPT-3, GPT-4, LaMDA, LLaMA, Stanford Alpaca, Google FLAN, Poe, Falcon LLM)
4. Image AI Models & Services (Midjourney, Stable Diffusion, ControlNet (SD))
5. Video AI Models (Runway - Gen 1 & 2, Kaiber, D-ID)
6. Audio AI Models (ElevenLabs)
1. Introduction to Prompt Engineering
2. Introduction to ChatGPT
3. Designing a prompt - The process and workflow
4. Avoiding prompt injections using delimiters
5. Defining constraints
6. Zero-shot Prompting
7. Few-shot Prompting
8. Persona Prompting
9. Chain of Thought
10. Adversial
11. Project: Data Analysis using AzureOpenAI
12. Project: Implement ML LifeCycle using AzureOpenAI
1. What is LangChain and when should you use it?
2. The LangChain Ecosystem
3. Supported LLMs
4. Case Study: Getting started with LangChain and OpenAI
5. Prompt composition and templates
6. Using multiple LLMs (Chains)
7. Working with Data loaders - Ingesting documents
8. Working with text splitters - Chunking Data
9. Working with Chains (Conversational Retrieval QA, Retrieval QA, Summarization, API etc.)
10. Working with Memory
11. Working with Embedding
12. Different Model Evaluation metrics like BLEU ,ROUGE
1. Introduction to RAG
2. Improve RAG ny reranking of context
3. Advanced RAG
4. Cache mechinism in RAG
5. Project: ChatBot implementation using RAG
1. Inteoduction to Different LLM llama2, Mistreal,Gemma etc
2. When and how to recalibrate,re-train,re-build models
3. Search Architecture
4. Chatbot Architecture
5. Domain specfic architectures
1. Use different Evaluation metrics like BELU, ROUGE-1 ,2 RAGAS
2. Costing of different Model
3. Solution to reduce Project Cost using Quantized LLM
1. Definition and Purpose of Fine-tuning, Preparing Data for Fine-tuning, Steps to Fine-tune an LLM, Transfer Learning in LLMs
2. Choosing an LLM for fine tuning from Huggingface Model. Understanding model speacifications.
3. Different PEFT technique like LORA,QLORA
4. Full Fine Tunning, Task Specfic Fine Tunning
5. Identifying Common Risks and Limitations, Bias and Fairness in LLMs, Mitigation Strategies for Bias, Ensuring Model Robustness
1. Use of Vector Database in Gen AI application
2. Vector DB vs Graph Database
3. Introduction to ChromaDB , FAISS,Milvus etc
4. Collection and metadata creation using chromaDB
5. Vector DB Benefits
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Generative AI Course Fees

SELF PACED LEARNING

US $ 399.00
Refund Policy
  • Duration : 35 hrs
  • Lifetime Free Upgrade
  • Reference Documents
  • 24x7 Support & Access

Online Class Room Program

US $ 699.00
100% Money Back Guarantee
  • Duration : 35 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 02 Jun 2026
  • Weekday Batch 08 Jun 2026
  • Weekend Batch 06 Jun 2026

Corporate Training

Corporate Training
  • Customized Training Delivery Model
  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support

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Generative AI Certification

Upon completion of igmGuru’s Generative AI Training, you will receive a Course Completion Certificate that authenticates your skills.

Generative AI Certification

Generative AI Certification Course FAQ's

Yes. The curriculum starts from basic AI concepts and gradually moves to advanced implementation.

Basic programming knowledge is helpful but not mandatory. We cover foundational implementation steps clearly.

Yes. You will work on hands-on projects, including AI chatbots, content automation tools, and LLM-based applications.

Yes. Prompt Engineering is covered from beginner to advanced levels.

Absolutely. The curriculum is designed around real-world use cases and enterprise implementation strategies.

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Rating 5/5 based on 5 reviews

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