Who is this for?
- Engineering and science graduates
- Developers moving into AI product roles
- Data-curious professionals shifting into ML
- Students targeting high-growth technology roles
Curriculum
Module 1
Python and Data Foundations
- Python for data workflows
- Pandas, NumPy, and data wrangling
- Statistical intuition for ML
Module 2
Machine Learning Core
- Supervised and unsupervised models
- Model validation and performance metrics
- Feature engineering strategies
Module 3
Deep Learning and LLM Basics
- Neural network fundamentals
- Computer vision and NLP intro
- Prompting and LLM application design
Module 4
Deployment and Portfolio
- Model serving with APIs
- MLOps overview and monitoring
- Capstone: end-to-end AI product
Career Outcomes
- Build and deploy applied ML solutions
- Portfolio with 2 production-style projects
- Starting packages: ₹4-7 LPA depending on stack depth
Career Support
- • Github project review sessions
- • Mock technical rounds for AI roles
- • Portfolio storytelling for recruiters
- • Referral support through partner network
Outcome proof you can show recruiters
2
Capstone projects
25+
Model experiments
Weekly
Code review cycles
150 days
Placement support window
Portfolio Proof Stack
- • End-to-end ML pipeline project
- • Inference API deployment walkthrough
- • Evaluation and model comparison report
Tools and roles you build toward
Tools You Practice
- • Python
- • scikit-learn
- • TensorFlow/PyTorch
- • FastAPI
Hiring Roles
- • ML Engineer - Junior
- • AI Application Developer
- • Data Science Analyst
- • Prompt Engineer
Salary Signals
- • Entry AI roles: ₹4-7 LPA
- • Product + deployment skills can accelerate growth to ₹8-12 LPA
- • AI portfolio quality heavily influences interview conversion
Program FAQ
Ready to enrol?
Batches fill fast. Reach out on WhatsApp or call us directly.
Without AI capability, teams automate slower, learn slower, and lose competitive velocity in data-driven markets.