ML core
- Supervised learning
- Pipelines
- Hyperparameter tuning
IT · Intermediate
AI & Machine Learning
Classical ML, deep learning foundations, and applied AI projects with offline placement focus.
OnlineOfflineJob assist · offline
About this program
Go from ML fundamentals to applied AI workflows: supervised learning, neural nets, NLP intros, and responsible model evaluation. Offline program includes job assistance and hiring-pipeline access.
Who this is for
Learners targeting ML Engineer (assoc.) or AI solution developer paths who want structured projects, mentor feedback, and offline placement coaching.
Target roles
- ML Engineer (assoc.)
- AI solution developer
- Data Science hybrid roles
Curriculum outline
Deep learning intro
- Neural networks
- CNNs/RNNs basics
- Transfer learning overview
Applied AI
- NLP intro
- Capstone
- Interview system design basics
What you'll walk away with
- Train and evaluate modern ML models
- Understand deep learning building blocks
- Ship applied AI demos for your portfolio
- Discuss trade-offs, bias, and model monitoring
Job assistance (offline)
Offline cohorts in this program include career roadmap, resume & portfolio reviews, mock interviews, and partner hiring activity. Online learners still graduate with projects and mentor feedback — talk to admissions about mixed paths.
How placement worksFAQ
AI & Machine Learning