How long does it take to become job-ready in Advanced AI?

Most learners become job-ready in Advanced AI within 8 to 12 weeks, depending on consistency and project work. A structured course with real-world AI projects, automation workflows, and portfolio building accelerates career readiness.

Is this Advanced AI course suitable for beginners and freshers?

Yes, this course is designed for beginners, freshers, and working professionals. You don’t need prior coding experience to start learning Generative AI, AI tools, and automation systems.

What tools and technologies are covered in this AI course?

This Advanced AI course covers: ChatGPT and Generative AI tools, AI agent development platforms, Automation tools and workflow systems, Prompt engineering frameworks, Real-world AI applications. These tools help you build scalable AI solutions and automation systems.

How long does it take to become job-ready in Advanced AI?

Most learners become job-ready in Advanced AI within 8 to 12 weeks, depending on consistency and project work. A structured course with real-world AI projects, automation workflows, and portfolio building accelerates career readiness.

Is this Advanced AI course suitable for beginners and freshers?

Yes, this course is designed for beginners, freshers, and working professionals. You don’t need prior coding experience to start learning Generative AI, AI tools, and automation systems.

What tools and technologies are covered in this AI course?

This Advanced AI course covers: ChatGPT and Generative AI tools, AI agent development platforms, Automation tools and workflow systems, Prompt engineering frameworks, Real-world AI applications. These tools help you build scalable AI solutions and automation systems.

10 Weeks Advanced AI & AI Agent Course

Learn Advanced AI, Generative AI, ChatGPT, and AI agent development through real-world projects, no coding background required.

2000+

Learners

4.8/5

Average Rating

Learners working across 1700+ companies

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Live Classes

Mentor Connect

Career Support

SOCIAL PROOF

Our Alumni Are Working Across 1700+ Top MNCs

Our learners are building AI agents, automation systems, and real-world AI applications across top companies in India.

PROGRAMS ROADMAP

Advanced AI Roadmap for Working Professionals & Freshers

2,000+ learners across India are transitioning into AI careers by building AI agents, automation workflows, and real-world AI systems.

Transition into AI Roles with a Structured Learning Path

CAREER SWITCHERS PATH

Leverage Your Existing Skills

Learn how to apply AI workflows to your current domain.

Learn AI Tools for Real Work

Master AI tools, automation systems, and workflows.

Build AI Workflows & Systems

Create AI-powered workflows and automation processes.

Work on Real Industry Projects

Build practical AI projects and apply AI in real scenarios.

Transition into AI Roles

Use your portfolio, and projects to move into AI-driven roles.

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PROGRAM BENEFITS

Designed for Real-World AI Execution

This is not a theory-heavy course. You learn by building AI agents, automation systems, and real-world AI applications.

Live Class

Mentored by
Top 1% AI Engineers

1:1 Mentorship from AI Experts

Learn directly from professionals working in AI, automation, and product roles.

Live Project-Based Learning

Build AI agents, workflows, and automation systems in real-time.

Industry-Relevant AI Curriculum

Stay updated with the latest AI tools, LLMs, and automation trends.

Career-Focused Learning Path

Move from learning to earning with job-ready AI skills and real projects.

PROGRAM CURRICULUM

What You Will Learn in This Advanced AI Course

This program covers everything from fundamentals to advanced AI agents, workflows, and automation systems

Week 1: Foundations of Advanced GenAI Engineering

Learning Goal

Deepen your understanding of modern LLM architectures and master the end-to-end data pipeline from raw ingestion to training.

Session 1: The Architecture of Modern LLMs

Transformer internals
Attention mechanisms
Local LLM execution

Lab: Run and interact with Llama 3 locally using Ollama or Hugging Face Transformers

Session 2: Data Pipelines for AI Training

Ingestion, Pre-processing
Information extraction
ETL for AI datasets

Lab: Create and visualize a custom training dataset for text generation

Session 3: Training a Small Transformer from Scratch

Layer construction
Tokenization basics
Forward/Backward pass implementation

Lab: Train a 2-layer Transformer to predict next words on a sample corpus

Week 2: Fine-Tuning and Evaluating LLMs

Learning Goal

Learn to optimize open-source models using techniques like LoRA and QLoRA for domain-specific data.

Session 4: Scaling Up – Fine-Tuning Open-Source Models

Full finetuning vs PEFT
LoRA & QLoRA mechanics
Optimization states

Lab: Fine-tune a small Llama-2 or Gemma model on domain-specific data

Session 5: Evaluation and Optimization

Perplexity & Human Eval
BLEU, ROUGE & Diversity metrics
Optimization loops

Lab: Evaluate your fine-tuned model against a reference dataset

Session 6: Efficient Model Serving

Model quantization
FastAPI serving
Local chatbot UI integration

Lab: Deploy your fine-tuned model as a local chatbot app

Week 3: Building a RAG System from Scratch

Learning Goal

Explore Retrieval-Augmented Generation by building pipelines connecting LLMs to external documents.

Session 7: RAG Architecture Deep Dive

Query input processing
Initial retrieval mechanics
Re-ranking strategies

Lab: Build a minimal RAG pipeline with FAISS and Llama 3

Session 8: Implementing Vector Search and Indexing

Vector databases (Chroma, FAISS)
Chunking strategies
Indexing optimization

Lab: Build and query a FAISS index with 1,000+ text chunks

Session 9: Hybrid Retrieval and Context Enhancement

BM25 integration
Hybrid search logic
Context window management

Lab: Integrate BM25 + FAISS to build a hybrid RAG retriever

Week 4: Scaling and Optimizing RAG Systems

Learning Goal

Advance RAG implementations by managing complex knowledge ingestion pipelines and metadata.

Session 10: Knowledge Ingestion and Context Management

Metadata ingestion
Ingestion pipelines
Context management strategies

Lab: Build a knowledge ingestion pipeline for domain documents

Session 11: Evaluation and Factual Consistency in RAG

Retrieval metrics
Generator faithfulness
Evaluation dashboards

Lab: Create an evaluation dashboard for your RAG assistant

Session 12: Multi-Domain Knowledge Bases

Domain-specific indexing
Multi-base retrieval
Cross-domain reasoning

Lab: Build a multi-domain RAG system (e.g., energy + HR data)

Week 5: Multimodal Generative AI

Learning Goal

Expand into vision and audio by integrating models like CLIP, BLIP, and Whisper.

Session 13: Image + Text Systems (CLIP & BLIP)

Image encoding
Text grounding
Multimodal alignment

Lab: Build a text-image captioning assistant

Session 14: Audio and Speech Systems (ASR + TTS)

Whisper integration
Piper for TTS
Audio summarization

Lab: Build a meeting transcriber that summarizes audio inputs

Session 15: Multimodal Integration Projects

Text+Image fusion
Insight generation
Multimodal reporting

Lab: Build a report generator that uses text + images for insights

Week 6: Core Agent Engineering (From Scratch)

Learning Goal

Transition from static models to reasoning agents using prompt chaining, memory, and state management.

Session 16: Agent Fundamentals

Prompt chaining
Reflective agents
Linear vs Graph workflows

Lab: Implement a simple 'Reflective Agent' in Python

Session 17: Memory, Tools, and State Management

Memory persistence
Tool calling logic
State machines

Lab: Add memory persistence to your text reasoning agent

Session 18: Building a Local Task Automation Agent

Planning & Goal setting
Information recall
Consistent responses

Lab: Build a local file management assistant

Week 7: Multi-Agent Collaboration

Learning Goal

Design ecosystems where specialized agents collaborate using frameworks like CrewAI and LangGraph.

Session 19: Multi-Agent Architecture

Planner-Executor workflows
Reflection & Monitoring
Trigger mechanisms

Lab: Create a planner + executor multi-agent workflow

Session 20: Using CrewAI / LangGraph for Agent Collaboration

Agent orchestration
Specialized roles
Document summarization systems

Lab: Build a multi-agent system for document summarization

Session 21: Domain-Specific Agents

Custom monitoring agents
Real-world data integration
Alert triggering

Lab: Develop a 'Power Plant Efficiency' monitoring agent

Week 8: Infrastructure, Safety and Deployment

Learning Goal

Prepare AI applications for real-world deployment with focus on safety, observability, and governance.

Session 22: Containerization and Model Deployment

Docker for agents
Deployment strategies
Scaling local models

Lab: Containerize your multi-agent project

Session 23: Monitoring and Logging AI Systems

Agent observability
Tracing & Logging
Performance analytics

Lab: Add observability to your deployed agentic app

Session 24: Safety, Bias, and Governance

Guardrails & Content filters
PII redaction
Responsible AI policies

Lab: Implement safety guardrails and content filters for your agents

Week 9: Capstone Development

Learning Goal

Build a full-scale multi-agent GenAI product from ideation to testing and demo.

Session 25: Capstone Ideation and Architecture Design

Product strategy
System architecture
Tech stack selection

Lab: Design the architecture for your multi-agent GenAI product

Session 26: Development & Implementation Support

Feature development
Debugging sessions
Integration testing

Lab: Implement core features of your capstone project

Session 27: Testing, Validation, and Demo Rehearsal

System validation
UI/UX polishing
Presentation rehearsal

Lab: Conduct final testing and prepare for the Demo Day

Week 10: Bonus Module - Specialized AI Agent System

Learning Goal

Explore autonomous AI systems that can search, reason, and report autonomously.

Session 28: Research & Knowledge Agents

Autonomous search
Source synthesis
Reasoning from documents

Lab: Build agents that autonomously search and summarize documents

Session 29: Operational Agents

Data monitoring
Trigger alerts
Automated reporting

Lab: Develop agents that monitor data and trigger reports

Session 30: Collaborative Ecosystem Agents

Combining agent suites
Cross-agent reasoning
Demo Day

Lab: Combine multiple agents into one collaborative ecosystem suite

MENTORS

Learn from Industry AI Experts

Get mentored by professionals with 10+ years of experience working in AI and automation roles across top companies.

Siddharth Sahani

Principal ML Tech Lead - Appodeal Inc

Ashu Mishra

Technical Product Manager - Zigram

TOOLS

Tools You Work With

Python

PyTorch

CUDA

Transformers

LangChain

LangGraph

CrewAI

Chroma

FastAPI

Docker

Llama 3

Mistral AI

Gemma

Ollama

Weights & Biases

Langfuse

Gradio

Blip

BGE m3

Bitsandbytes

Clips AI

Datasets

Instructor XL

Peft

LoRA

LLM

Python

PyTorch

CUDA

Transformers

LangChain

LangGraph

CrewAI

Chroma

FastAPI

Docker

Llama 3

Mistral AI

Gemma

Ollama

Weights & Biases

Langfuse

Gradio

Blip

BGE m3

Bitsandbytes

Clips AI

Datasets

Instructor XL

Peft

LoRA

LLM

Python

PyTorch

CUDA

Transformers

LangChain

LangGraph

CrewAI

Chroma

FastAPI

Docker

Llama 3

Mistral AI

Gemma

Ollama

Weights & Biases

Langfuse

Gradio

Blip

BGE m3

Bitsandbytes

Clips AI

Datasets

Instructor XL

Peft

LoRA

LLM

Python

PyTorch

CUDA

Transformers

LangChain

LangGraph

CrewAI

Chroma

FastAPI

Docker

Llama 3

Mistral AI

Gemma

Ollama

Weights & Biases

Langfuse

Gradio

Blip

BGE m3

Bitsandbytes

Clips AI

Datasets

Instructor XL

Peft

LoRA

LLM

SKILLSET

Advanced AI Skill Checklist

Discovery

Understand AI fundamentals, LLMs, and identify real-world AI use cases.

PHASE 01

Strategy

Design AI-first solutions and automation strategies.

PHASE 02

Design

Build structured AI workflows using prompt engineering and system design.

PHASE 03

Build

Create AI agents, automation systems, and real-world applications.

PHASE 04

Analyze

Optimize AI outputs and improve performance using feedback loops.

PHASE 05

Grow

Scale AI systems across use cases and deploy advanced automation.

PHASE 06

CERTIFICATION

The Certificate Recognized By The Industry

Get Your Nano-Degree in Advanced Generative AI

Show the world your expertise in Advanced Generative AI and autonomous agents, stand out in a competitive job market and get hired easily.

Industry-recognized Nano Degree in Advanced Generative AI.
Verified badge + unique verification ID
Trusted by 2500+ companies and agencies
Advanced Generative AI Projects portfolio
Lifetime exclusive alumni community access

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CAREER

Career Opportunities After This AI Course

AI Agent Developer

₹10L – ₹28L

Deploy and optimize large language models

AI Workflow Automation Specialist

₹12L – ₹30L

Design scalable AI infrastructures & pipelines

Conversational AI Designer

₹10L – ₹25L

Build autonomous AI agents for real-world tasks

Generative AI Solutions Specialist

₹15L – ₹35L

Solve business problems using advanced AI systems

Companies are actively hiring professionals with AI, automation, and LLM expertise.

AI ENGINEERING PATH

TRADITIONAL ENGINEERING

₹50L

₹40L

₹30L

₹20L

₹15L

₹10L

₹0L

20262028203020322035

PROJECTS

Build Real AI Projects That Get You Hired

Instead of just completing assignments, you build:

AI agents

Automation systems

AI workflows

Real-world AI applications

This becomes your proof of work. What recruiters actually care about.

PRICING

Choose Your Advanced AI Learning Path

Flexible pricing options designed for professionals who want to master Advanced AI tools, agents, and automations.

AcceleratorX + IBM

An advanced AI program in India designed for learners seeking expertise in AI agents, automation systems, and real-world AI applications.

₹ 59,999 + GST

ADVANCED AI CAREER TRACK

Everything included in the Regular Program
IBM Industry Certification
Advanced Agentic AI Track
Exclusive Multi-Agent Strategy Workshops
Production-Grade Case Simulations
Dedicated Career Support and Mentorship

Regular Program

A complete AI course for beginners and professionals focused on AI tools, workflows, and automation.

₹ 44,999 + GST

FULL PROGRAM ACCESS

Complete AI Curriculum
Hands-on Projects
Ai Workflow Building
Tools and Framework Access
Mentorship

FAQs

The best Advanced AI course in India is one that focuses on real-world applications, AI agent building, and automation workflows. AcceleratorX offers a hands-on Advanced AI course designed for working professionals to build AI systems, automate business processes, and transition into high-paying AI roles without relying on outdated theory.

In an Advanced AI course, you will learn Generative AI, ChatGPT, prompt engineering, AI agents, and automation workflows. You will also build real-world AI systems, design workflows, and implement AI solutions used in companies for decision-making and productivity.

Yes, you can learn Generative AI and AI agents without coding. This course starts with no-code AI tools, prompt engineering, and automation platforms, making it suitable for beginners, non-technical professionals, and freshers.

After completing an Advanced AI course in India, you can apply for roles such as: AI Agent Developer, Generative AI Specialist, AI Workflow Automation Specialist, Conversational AI Designer, AI Product Associate. These roles are in high demand across startups, MNCs, and tech-driven companies.

The average salary for AI professionals in India is: Entry-level: ₹8–12 LPA, Mid-level: ₹12–25 LPA, Experienced: ₹25–70 LPA. Professionals with skills in Generative AI, AI agents, and automation workflows often earn significantly higher due to increasing demand.

Start Your AI Career Before You Fall Behind

Join professionals building AI-powered careers using advanced AI systems, agents, and automation.