This live online Generative AI course takes you from machine learning apps and deep learning foundations into transformers, LLM applications, RAG, multimodal systems, agentic workflows, fine-tuning, quantization, and production serving.
Instead of teaching only one layer, the program connects the full stack: transformer internals, ViTs, diffusion models, vector retrieval, evals, inference optimization, and deployment. You graduate with portfolio projects that show hiring teams you can design and ship end-to-end GenAI systems.
Deep, engineering-led, full-stack GenAI training.
12 modules • 200+ hours • 30+ tools • Certificate included • Placement support
Generative AI Training Overview
A quick summary of the format, duration, core skills, projects, certificate, and support included in this live online Generative AI course.
Format
Live Online Training
Duration
6 Months
Best For
Engineers, developers, ML learners and working professionals
Core Skills
LLMs, RAG, fine-tuning, agents, multimodal AI and model serving
Projects
RAG app, fine-tuned LLM, multimodal app and production API
Certificate
Included
Support
Placement and career support
Core Concept
Generative AI is a category of artificial intelligence that creates new text, images, code, audio, and video by learning patterns from large datasets. Unlike traditional ML that classifies or predicts, generative models produce original outputs. The core architectures powering this field today are:
Large Language Models
LLaMA, GPT, Gemini, DeepSeek
Diffusion Models
Stable Diffusion, DALL-E, AnimateDiff
Vision-Language Models
CLIP, LLaVA, Qwen-VL, Kosmos-2
Code Generation
CodeLLaMA, StarCoder2, DeepSeek-Coder
Your Portfolio
This Generative AI Course gives you a portfolio that grows from AI foundations to production GenAI systems. You will start with model training and evaluation, then move into transformers, RAG, fine-tuning, multimodal AI and a production-style agentic AI system.
Build a practical ML workflow with data preparation, model training, evaluation metrics and experiment comparison so you understand how AI systems are measured before moving into GenAI.
Work with neural networks, CNN/RNN concepts and transformer-based architectures to understand how modern AI models learn from text, images and sequences.
Build a retrieval-augmented generation system with document ingestion, chunking, embeddings, vector search, reranking, source citations and grounded LLM responses.
Adapt an open-source language model using LoRA / QLoRA concepts, evaluate outputs on custom tasks and understand when fine-tuning is better than prompting.
Build an AI system that works with text, image or document inputs using vision-language models and multimodal inference workflows.
Build a production-style GenAI system with agents, tool use, RAG or memory support, API serving, logging, evaluation checkpoints and a demo-ready workflow.
This Generative AI learning journey starts with Python, math and machine learning foundations, then moves into neural networks, transformers, LLMs, RAG, fine-tuning, multimodal AI and production deployment. You do not need to be an expert before joining, but you should have interest in coding and building real AI systems.
Start with Python for AI, basic math intuition, data handling, machine learning concepts and model evaluation before moving into deep learning and GenAI.
Learn neural networks, CNNs, RNNs, attention mechanisms and transformer architecture so you understand the model-side foundation behind modern AI systems.
Build LLM applications, retrieval-augmented generation systems, prompt workflows and fine-tuning experiments using practical GenAI engineering patterns.
Work with multimodal AI, agent workflows, model serving, APIs, evaluation, deployment basics and production-ready AI system design.
By the end, you’ll be ready for roles like GenAI Engineer, LLMOps Specialist, or AI Research Developer — capable of designing systems like RAG-powered assistants, multimodal AI apps, or enterprise copilots from scratch.
This Generative AI course is designed for developers, ML learners, freshers, and working professionals who want a structured path into LLMs, RAG, fine-tuning, multimodal AI, agents, and production serving.
Build production-ready GenAI applications on top of your coding background.
Extend ML and deep learning fundamentals into modern Generative AI systems.
Follow a structured path from foundations into deployable GenAI projects.
Upskill without losing the technical depth needed for real AI work.
Basic Python is helpful, but you do not need prior LLM or deep learning experience to start. The course begins with ML and deep learning fundamentals before moving into advanced Generative AI systems.
Helpful to have
You will build up during the course
This Generative AI Course also builds the practical skills needed for AI Engineer roles, including ML foundations, neural networks, CNNs, RNNs, Transformers, ViTs, VLMs, RAG, agents, fine-tuning, quantization and model serving.
Build the mathematical and machine learning base needed for applied Generative AI work.
Understand the deep learning building blocks that lead into modern transformer systems.
Learn attention, tokenization, prompting, and model behavior across modern language models.
Work with ViTs, VLMs, and multimodal pipelines that combine text and visual understanding.
Build retrieval pipelines with chunking, embedding, filtering, ranking, and grounded responses.
Create tool-using agent systems that can reason, call APIs, and orchestrate multi-step tasks.
Customize models with LoRA, QLoRA, and related techniques for domain-specific use cases.
Reduce inference cost and improve deployment efficiency with practical optimization methods.
Serve production-ready models through APIs with scalable inference patterns and deployment workflows.
Measure reliability, quality, and safety using structured evaluation and monitoring workflows.
Ship applications that combine text, image, and speech inputs in one AI product workflow.
Connect models, serving, infrastructure, and product-facing APIs into deployable systems.
The Generative AI tools and frameworks covered in this course are the same ones used in production AI systems at leading companies. You will work hands-on with over 36 tools across training frameworks, orchestration libraries, vector databases, serving infrastructure, API providers, and observability platforms.
Core deep-learning framework for model training and research
Structured training loops, multi-GPU scaling, and distributed training
Pre-trained models, tokenizers, and training pipelines for NLP and vision
Diffusion model pipelines for image, video, and audio generation
Parameter-efficient fine-tuning (LoRA, QLoRA) and RLHF/DPO training
Distributed training and inference optimization for billion-parameter models
The Generative AI models covered in this course are production-grade architectures you train and fine-tune directly, not just call through APIs. They include large language models like LLaMA and DeepSeek, vision-language models like Qwen-VL, diffusion models like Stable Diffusion XL, and coding models like StarCoder2.
From 8B to 400B+ parameters — understand scaling laws, context-window expansion, and what makes each architecture unique.
Models that reason across images, text, and speech — powering visual QA, document understanding, and cross-modal search.
Text-to-image, text-to-video, and creative AI — the generative media stack driving modern design and content pipelines.
Purpose-built for software engineering — code generation, debugging, refactoring, and inline copilot experiences.
Customizing and deploying Generative AI models involves four core technique areas: parameter-efficient fine-tuning with LoRA and QLoRA, alignment using RLHF and DPO, retrieval-augmented generation with hybrid and graph-based RAG patterns, and inference optimization through quantization, KV-cache management, and speculative decoding for production serving.
LoRA · QLoRA · PEFT · DAPT · SFT
What: Inject low-rank adapters into target layers instead of retraining the entire model.
Why: Fine-tune billion-parameter models on consumer GPUs with rapid iteration and minimal compute.
RLHF (PPO) · DPO · ORPO · RLAIF
What: Steer model behavior toward human-preferred outputs using reward signals and preference pairs.
Why: Safer, more controllable generation — critical for production copilots and enterprise deployment.
Hybrid RAG · Graph-RAG · Fusion RAG · Re-ranking
What: Ground LLM responses with external knowledge via chunking, embedding, retrieval, and citation.
Why: Accurate, hallucination-resistant answers for legal, healthcare, and enterprise QA systems.
KV-Cache · Speculative Decoding · MoE Routing · Quantization
What: Maximize throughput and minimize latency with attention-aware caching, draft-model decoding, and expert routing.
Why: Production-grade speed at lower cost — serve thousands of concurrent requests efficiently.
High-throughput inference with PagedAttention and continuous batching
Draft-model decoding for 2–3× latency reduction
Mixture-of-Experts routing for compute-efficient scaling
Tracing, evaluation, and observability pipelines for reliability
Containerized deployment with CI/CD and auto-scaling
Edge and on-device deployment for offline and low-latency use
The Generative AI course roadmap is a 12-module, 24-week structured learning path. It begins with Python and math foundations, progresses through neural networks, deep learning, and transformer architectures, then covers LLMs, diffusion models, multimodal AI, fine-tuning, RAG, quantization, RLHF, and agentic AI systems.
The Generative AI course syllabus is a comprehensive, section-by-section breakdown of every topic, tool, and technique covered across 24 weeks of live instructor-led training. It includes ML foundations, neural networks, deep learning, transformers, LLMs, RAG, fine-tuning, multimodal AI, agents, quantization, model serving, and deployment.
Generative AI Course with Certification
Upon completing the Generative AI course requirements, you receive a School of Core AI course completion certificate. It reflects hands-on work across LLMs, VLMs, diffusion models, RAG pipelines, fine-tuning, alignment, and production-grade serving, and each certificate includes a unique verification ID and QR code.
CERTIFICATE
OF ACHIEVEMENT
THIS IS TO CERTIFY THAT
SHWETHA SHARMA
Date : 25th Jan 26
Has Successfully Completed The
6-Month Comprehensive Generative AI Training Program
Conducted By The School Of Core AI.
This Intensive Program Included Hands-On Training In Python, Deep Learning, Transformers, LLMs, VLMs, Stable Diffusion, RAG Pipelines, Fine-Tuning (LoRA/QLoRA), RLHF/DPO Alignment, Model Serving (vLLM/TGI), And Agentic AI Fundamentals.
Aishwarya Pandey
Founder and CEO
Certification ID :
SHWETASHARMA250126
Each certificate is verifiable with a unique ID and QR code. Share it on LinkedIn, include it in your portfolio, or present it during interviews.
This Generative AI program is built for engineers who want production-level depth, not surface-level overviews. It covers the latest patterns including MCP, hybrid RAG, and production-grade evaluation. The comparison below shows how the curriculum, tools, projects, and career support differ from typical Generative AI courses.
| Feature | Our Program | Other Courses |
|---|---|---|
| LLMs & RAG Modules | ✔ End-to-end RAG: retrieval setup, tool use, function calling, grounding & citations. | ✘ Prompt-only demos; little grounding or retrieval planning. |
| MCP (Model Context Protocol) & Tool Interop | ✔ Portable tool adapters, unified context, and vendor-neutral integration patterns. | ✘ No MCP; tight coupling to a single SDK/provider. |
| Hybrid RAG & Re-Ranking | ✔ BM25 + dense + metadata filters with re-ranking, multi-hop queries, and eval. | ✘ Single-vector lookups; weak grounding and quality checks. |
| Project-Based Curriculum | ✔ 20+ projects: fine-tuning, multimodal apps, domain RAG, internal copilots. | ✘ 1–2 assignments; no real deployment. |
| Deployment & Scaling | ✔ FastAPI, Docker, Kubernetes; vector DBs; CI/CD and autoscale practices. | ✘ Notebook demos; no infra readiness. |
| Tooling Ecosystem Mastery | ✔ Hugging Face, Diffusers, OpenAI SDKs, Pinecone, Chroma, FAISS. | ✘ Surface-level API coverage. |
| Evaluation, Tracing & Guardrails | ✔ RAGAS/DeepEval, LangSmith/LangFuse, policy tests, PII filters, regression suites. | ✘ Minimal evaluation; no traceability/safety gating. |
| Live Mentorship & Expert Sessions | ✔ Weekly live classes, 1:1 mentorship, office hours with AI engineers. | ✘ Pre-recorded only; no technical mentorship. |
| Placement Support & Career Guidance | ✔ Portfolio reviews, resume feedback, interview prep, and a verifiable course certificate. | ✘ Limited career guidance and less project-focused support. |
Clear pricing for the 6 month live online Generative AI course, including guided projects, certificate, and learner support.
One-time payment
₹64,999
6 months • Live ILT • Capstone • Certificate
Generative AI course fees are 64,999 INR for a 6 month live instructor-led training program with sessions, guided projects, capstone demo, and verifiable certificate.
The course is structured to help serious learners build a credible portfolio, prepare for interviews, and continue learning alongside their current work or academic schedule.
Career Support
Placement support is built around portfolio quality, interview readiness, and applied project work. The focus is on helping you present your Generative AI skills clearly rather than making unsupported hiring promises.
Learner Fit
This course is suitable for working professionals who want depth without losing flexibility. Live online instruction, mentor support, and recordings make it easier to keep momentum while balancing a job.
Career Outcomes
The course stays focused on Generative AI, while also building practical role-specific skills used in AI engineering, LLM engineering, and production AI application work.
Skills Cluster
LLMs, RAG, Agents
Role Path
Skills Cluster
ML, Deep Learning, Transformers
Role Path
Skills Cluster
Fine-tuning, Quantization
Role Path
Skills Cluster
APIs, Serving, Deployment
Role Path
Skills Cluster
Multimodal AI, VLMs
Role Path
Career outcomes depend on your current background, portfolio quality, interview preparation and hiring market conditions.
Testimonials from Generative AI course graduates reflect real experiences of professionals who transitioned into AI engineering roles. These learners completed the full program including LLMs, fine-tuning, RAG, and deployment, and now work as Gen AI engineers, deep learning engineers, and AI research engineers.
Hear real experiences from professionals who've completed this course
If you want to go deeper after finishing this Generative AI course, these are the four most relevant next-step courses for application development, model-side depth, agent workflows, and production AI operations.
For software developers who want to build AI applications using LLM APIs, RAG, agents and backend workflows.
View AI Developer CourseFor learners who want deeper LLM understanding, prompting, LLM workflows and model-side language AI concepts.
View Large Language Model CourseFor learners who want to build agent workflows, multi-agent systems, LangGraph, CrewAI, MCP and automation systems.
View Agentic AI CourseFor learners who want production AI operations, monitoring, reliability, observability and automation.
View AIOps CourseCompare Before You Enroll
These pages separate broad generative AI learning from adjacent developer and agentic paths.
See when a project-led developer path beats a deeper GenAI specialization and when it does not.
Open comparisonChoose between broad GenAI foundations and agent-focused orchestration depth.
Open comparisonThese FAQs cover certification, placement support, syllabus depth, fees, duration, working professional fit, RAG, fine-tuning, deployment, and how this course differs from the AI Developer and Agentic AI paths.
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