First: understand
Make the invisible visible.
Start with the ideas your project needs. Build a small first version and inspect what works.
first_versionworkingtest_statusneeds review
Live online, guided project-based AI learning
Understand AI.Build it.Explain it.
Prepare for your next internship, capstone project or technical interview—by building an AI system you can actually explain.
Live onlineLevel 1–312–20 live hours
The video platform, recording access and missed-class policy will be confirmed before paid enrolment opens.
AI tools can help write an app. Witmint helps you decide what to build, test what breaks, and explain why it works.
The method
Learn the concept. Build the project. Show the evidence.
Every milestone starts with a concept, moves into a guided build, and ends with evidence you can discuss.
Next: implement
Build with guidance.
Apply each concept in a guided task, from Python foundations to AI application patterns.
Then: test
Test what you built.
Try realistic cases, inspect mistakes and record what improved.
Finally: ship
Ship and explain.
Package your repository and working model, then explain the decisions behind them.
git push✓ deployed
Try the sample · choose a question
Follow an answer
back to its source.
Select one of the three questions to see how a supported answer differs from a guess. This is a scripted teaching example with fictional data, not a live AI model or a measured course result.
WKnowledge EngineFictional example
Revenue grew 14% year-on-year, driven by the company’s enterprise segment.
[1]Fictional report · §1 RevenueView source ↗
Who this is for
Work you can put in front of someone.
From first-time coders to experienced builders: choose Level 1, 2 or 3 based on your starting point. Projects can support internships, capstones, interviews and research work.
Next internship
Practise a real workflow: branch, bug, meaningful commit, reviewed pull request and concise handover.
Capstone project
Scope a problem, defend your architecture, show evaluation, and explain your individual contribution.
Technical interview
Explain a design choice, a failure case and the test that made your project more credible.
Research projects
Adapt the evaluation method for a paper or PhD defence: compare a baseline, cite evidence and state the limits.
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Try the dated booking flow
Choose your training.
Choose your date.
On staging, send a request for one complete training. We will test the journey before accepting live enrolments.
Your next step
One training. Your next step.
Choose a full-course batch and check its dates in your cart. Save a training to your wishlist when the timing does not fit.
Explore training dates →Clear before you commit
Do I need AI experience?+
No for Level 1. Level 2 expects basic Python, and Level 3 expects a small AI project or equivalent experience. You can check the starting point on each course page.
How do I enrol in a training?+
Choose a batch and add the complete training to your cart. We check your dates before payment. Courses appear in My Courses after payment is verified.
Will this get me an internship?+
You will build practical evidence for applications and interviews. The course does not promise an internship, placement or research acceptance.
What happens after the project?+
You retain your source repository, receive one project review and have a defined period of hosted-demo access. Limits are published before booking.