All journeys
Boarding
Guided cohort

AI & Machine Learning

Intelligence Run

A guided learning journey with a clear weekly rhythm, practical milestones and a conductor keeping the cohort moving together.

Boards

16 November 2026

Departure

10:00 am WAT

Conductor

Chidi Nwosu

Path

AI & Machine Learning

Cohort size

35 riders

Destination

Junior ML Practitioner

Project spotlight
Challenge preview

Featured cohort success

Intelligence Run signature project

Riders will turn their AI & Machine Learning learning into a practical team project, collecting decisions, feedback and measurable evidence for a portfolio-ready case study.

Intelligence Run riders collaborating on their signature project

10

Riders involved

3

Gated stages

1

Portfolio case study

Trip highlights

What riders experience together

Practical builds

Riders apply Python, Data preparation, Machine learning through guided tasks and portfolio work.

Live feedback

Weekly clinics with Chidi Nwosu turn blockers into clear next steps.

Mastery checks

Every stage includes a practical gated assessment and mentor review before riders continue.

Peer learning

10 riders share progress, review work and solve problems as a cohort.

Journey so far

What learners are doing now

Boarding

Riders are completing onboarding, setting up their tools and meeting their learning crew before Python and data begins.

Next stop

Stage 1

Python and data

Coming next: Build the core concepts through guided lessons and short exercises.

Next stop

Stage 2

Machine learning models

Coming next: Apply your skills in practical labs with peer feedback.

Next stop

Final stage

Responsible AI capstone

Coming next: Bring everything together in a portfolio-ready capstone.

Weekly schedule

How the cohort travels

MondayAnytimeWeekly module opens
Self-paced
Wednesday6:30 pm WATML lab
Live
Saturday10:00 am WATMentor check-in & project review
Community

From the road

Gallery, updates and showcases

A window into working sessions and useful public references. Gallery images illustrate the intended cohort feed; external work is credited and is not presented as Tech Bus Stop learner work.