Your First Trained Models,
Built the Right Way
Python, the math behind AI, and classical machine learning — regression, classification, clustering, and how to actually evaluate a model.
Months
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Instructor-Led Hours
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Certificate
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About The Programme
Two Courses. Your Choice.
Each course stands on its own and is job-ready by itself. We recommend doing both, but the pace, budget, and timing are up to you. Start with Part 1 now and move to Part 2 whenever you’re ready.
Foundations of AI & Python
Programming, math, and core AI concepts — starting from zero.
- 1 Month
- Beginner
- Hands-on exercises every week
- Introduction to AI & Python Basics
- Python for Data Science
- Mathematics for AI
- Introduction to Machine Learning
Intermediate Machine Learning
From your first model to a set of classical ML skills you can actually use.
- 1 Month
- Beginner-to-intermediate
- 3 projects, 1 team final project
- Supervised Learning
- Unsupervised Learning
- Model Optimization
Who Should Enrol
Complete beginners — no coding experience needed.
Career-switchers moving into tech without a coding background.
Anyone testing the water before committing to Applied AI & Deployment.
What You Will Achieve
Outcomes
- You'll train and evaluate regression, classification, and clustering models.
- You'll walk away with a small, GitHub-ready portfolio built from real mini-projects.
- You'll be comfortable reading and writing Python for data and ML work.
How You're Evaluated
- Assignments — 20%
- Mini Projects — 20%
- Internal Hackathon — 10%
- Final Project — 40% (the end-to-end ML pipeline, built as a team in Week 8)
- Final Viva — 10%
Where This Leads
- Leads naturally into Applied AI & Deployment — optional, not required.
- Career roles you'll be ready for: Junior ML Engineer, AI/ML Trainee, Data Analyst (ML-aware)
Built the way AI teams actually work, not the way a classroom does
Weeks 1–4 build the fundamentals hands-on — Python, the math behind AI, and your first models, with exercises every week, not just theory. From week 5, you’re training and evaluating real models in Jupyter: supervised learning, unsupervised learning, model optimization — the same setup working ML engineers use. In Week 8, you team up with 3–5 people to build an end-to-end ML pipeline from scratch — cleaning data, choosing a model, tuning it, breaking it, fixing it — then defending it in a technical viva, same as you’d do on the job.
Tools
Python
Jupyter Notebook
NumPy
Pandas
scikit-learn
Git & GitHub
FAQs
No. This course starts from zero.
Yes — a standalone certificate for this course alone.
No. Each course stands on its own.