AI / Machine Learning Engineering
Industry-aligned training focused on building intelligent systems using machine learning techniques, real datasets, and mentor-led project execution.
✔ Real ML Projects
✔ Weekly Evaluations
✔ Career Guidance
Who Should Join This Program?
Designed for learners serious about building a real tech career
✔ College students & fresh graduates (with basic programming knowledge)
✔ Software developers moving into AI roles
✔ Data analysts upgrading to ML
✔ Career switchers entering AI/ML
✔ Professionals aiming for applied AI roles
Career Paths After Completing?
Roles our learners prepare for through hands-on projects
✔ Machine Learning Engineer
✔ AI Engineer (Junior)
✔ Data Scientist (Entry-level)
✔ Applied ML Developer
✔ AI Research Assistant
Program Duration & Fees
3 Month
(Core)
Starting from 9,999
Includes:
Python (for ML) • Math Foundations (Linear Algebra & Statistics – Intro) • Data Handling with Pandas & NumPy • Data Visualization (Matplotlib / Seaborn) • Machine Learning Concepts • Supervised vs Unsupervised Learning • Model Training Basics • Jupyter Notebooks • Git & GitHub (Basics)
6 Month
(Advanced)
Starting from 19,999
Includes:
Python (Advanced for ML) • Scikit-learn • Feature Engineering • Model Evaluation & Tuning • Regression & Classification Models • Clustering Algorithms • Data Preprocessing Pipelines • ML Project Structure • SQL (Basics for ML) • Model Deployment (Intro) • Git & GitHub • ML Case Studies
9 - 12 Month
(Professional)
Starting from 29,999
Includes:
Advanced Python for ML • Advanced Statistics & Probability • Scikit-learn (Advanced) • Deep Learning (Intro with TensorFlow / PyTorch) • Neural Networks • Model Optimization • Natural Language Processing (Intro) • Computer Vision (Intro) • ML Pipelines & MLOps (Intro) • Model Deployment (APIs & Containers) • Data Versioning • Experiment Tracking • Cloud ML (Intro) • AI Ethics & Bias • Real-World ML Projects • Portfolio & Interview Preparation
Ready to Build a Career in AI & Machine Learning?
Learn machine learning by building real models and systems — not just studying theory
No obligation. Just a friendly conversation about your learning and career goals