Included with the couse

1. Complimentary eBook
Python from Scratch – A Practical Guide for AI Beginners
A structured refresher designed to help learners align Python skills with GenAI system development.

2. Comprehensive Course Material
Well-structured learning resources for every module, including explanations, architecture notes, and fully working code used during the sessions.

3. Alumni Status on Completion
Upon successful course completion, participants become part of the AI Mentorship Hub alumni network.

4. GitHub Portfolio — 19+ Projects
Every module ends with a real project published to your GitHub. By the end, you have a live portfolio of deployed AI systems, not just certificates.

5. Ongoing Industry Updates & Job Opportunities
Alumni receive continuous updates on industry trends, emerging GenAI roles, and relevant job opportunities shared through the community.

6. Alumni Privileges & Discounts
Eligible alumni discounts on future courses and advanced programs, as applicable.

Why AI/ML Engineering Now

Explosive Demand for AI Engineers

Companies are no longer just adopting AI — they are building it. ML engineers, LLM application developers, and MLOps engineers are among the fastest-growing roles in India's tech market. Organizations need people who can build, deploy, and maintain AI systems in production

Engineering Skills Separate Learners from Builders

Most people who learn AI stop at notebooks and tutorials. The roles that pay — and the careers that grow — belong to engineers who can deploy models as APIs, containerize applications, build RAG pipelines, and ship AI agents. This program is built to close that gap.

GitHub Portfolio Is the New Resume

In AI hiring, what you have built matters more than what you have studied. A live GitHub portfolio with deployed ML models, LLM applications, and documented architecture is the strongest signal you can send to any technical interviewer or hiring manager.

"The engineers who build AI systems will define the next decade of technology — not the ones who only use them."

Who This Course Is For

• Freshers and final-year CS/IT students preparing for AI/ML engineering roles

• Backend or software developers looking to transition into AI and ML

• IT professionals who write code but have no prior machine learning experience

• Developers who want to work with LLMs, RAG pipelines, and AI agents professionally

What You Will Build

• Loan Default Prediction System: Train and compare ML classifiers on financial data, evaluate with precision and recall, and document model selection reasoning

• Customer Churn Predictor with Explainability: Build an XGBoost model, tune it with Optuna, explain every prediction using SHAP values, and deploy as a FastAPI + Streamlit application

• Containerized AI App on AWS: Dockerize the churn prediction app, push to DockerHub, deploy to AWS EC2, and set up MLflow experiment tracking

• Domain-Specific RAG Assistant: Build a working retrieval-augmented generation pipeline over real documents using LangChain, ChromaDB, and an LLM API

• Capstone: End-to-End AI/ML Engineering Project — Your Choice

Course Journey

→ Python from zero to production-ready code 
→ Machine learning with Scikit-learn, XGBoost, LightGBM
→ Model deployment using FastAPI, Flask, Gradio, Streamlit
→ Containerization and cloud deployment with Docker and AWS
→ NLP: text classification, sentiment analysis, topic modelling, NER
→ Deep learning and computer vision using CNNs and transfer learning

→ LLM engineering: RAG pipelines, LangChain, AI Agents

→ Responsible AI, model governance, and EU AI Act awareness

→ 19+ real projects published to GitHub across all 7 parts

Why AI/ML Engineering With Us

Build your AI engineering career the right way — by designing and deploying real systems, not running pre-built notebooks. This program focuses on hands-on engineering, production deployment, and a learning experience that mirrors how AI is actually built and shipped in industry.

Real-World Projects

Expert Mentorship

Personalized Learning

Learn directly from data leaders with decades of real business and technical experience.

Small batches, focused guidance, and a completion certificate that boosts your career.

Apply every concept to live, industry-based projects and build a job-ready analytics portfolio

Frequently asked questions

I have never written Python before. Can I do this?

Yes. The program starts from absolute zero. Module 1 covers Python from scratch — variables, loops, functions, and file handling. You do not need any prior programming experience to begin.

How is this different from a data science course?

This program is built for engineers, not analysts. You will not spend time on Excel, dashboards, or business reporting. The focus is on building and deploying ML models, LLM applications, RAG pipelines, and AI agents — and putting them into production.

Will I actually deploy real applications?

Yes. Deployment is not optional or theoretical here. You will deploy a FastAPI backend, containerize an app with Docker, push it to AWS EC2, and deploy an LLM application to HuggingFace Spaces or AWS — all within the

Do I need cloud accounts or paid tools?

Most tools used in this program have free tiers sufficient for learning. AWS Free Tier covers the cloud modules. LLM API costs during the course are minimal. You will be guided on setup and cost controls from day one.

Will I get the recordings of the class?

Yes. All sessions are recorded and available in the LMS within 24 hours. You also get downloadable course materials, an interview preparation guide, and special topic modules.

The LMS includes an AI learning assistant personalised to your progress, an AI resume builder, and a Resume Tuner that adapts your resume for specific job openings in minutes. Access is yours for the duration of the program.

Will this help me get an AI/ML engineering job?

The program is designed around what hiring managers actually test: can you build and deploy an AI system? Your GitHub portfolio with 19+ real projects will be your strongest asset in any technical interview or job application.

What roles does this program prepare me for?

ML Engineer, AI Engineer, LLM Application Developer, MLOps Engineer, NLP Engineer, Computer Vision Engineer, AI Solutions Developer, AI Product Builder.

How many seats are available per cohort?

Each cohort is intentionally limited to around 20 participants to maintain interaction quality and hands-on guidance.

Is this course focused on jobs or real-world skills?

Both, and they are the same thing here. Every project is built around a real engineering problem. The skills you demonstrate through your GitHub portfolio are exactly what technical interviewers and hiring managers look for.