Three Tracks, One Steady Method
Whether you are just starting out or preparing a serious portfolio piece, there is a structured track built for where you are today.
Back to HomeHow Each Track Is Built
Every track at Backprop Labs follows the same underlying method: break a large skill into small, testable experiments, then repeat with feedback until the pattern sticks. Lessons are sequenced so ideas from Python fundamentals carry forward into data handling, and data handling carries forward into model building.
Quality assurance happens through mentor review rather than automated grading alone — a real person looks at your notebooks and gives specific notes. Typical timelines range from a few weeks for the Starter Lab Course to several months for the Applied AI Research Studio, depending on your pace and prior background.
This shared approach means you can move between tracks as your confidence grows, without having to unlearn habits from a different teaching style.
AI Starter Lab Course
A gentle beginner course covering Python, data handling, and the core ideas behind machine learning through small guided experiments. Built for newcomers who want a calm, structured start.
- Recorded lessons you can revisit anytime
- Practice notebooks for each topic
- Friendly community space for questions
Process: Watch a short lesson, complete the matching notebook exercise, then move to the next topic once you feel ready.
Machine Learning Experiment Track
A structured programme where you run and refine a series of model experiments on real data, with mentor feedback and code reviews. Suited to those with basic Python who want steady, hands-on progress.
- Live sessions with instructors
- Real project briefs based on real data
- A developing portfolio of experiments
Process: Receive a project brief, build and test a model, then walk through the results with a mentor before refining further.
Applied AI Research Studio
An in-depth studio for building a full applied project end to end, guided by mentors and peer review at each stage. Aimed at committed learners preparing a strong portfolio piece.
- One-to-one mentoring throughout
- Detailed project walkthroughs
- A shareable completion record
Process: Define a project scope with your mentor, build it in stages with regular check-ins, then present the finished work for peer review.
Which Track Fits You?
| Feature | Starter Lab | Experiment Track | Research Studio |
|---|---|---|---|
| Prior experience needed | None | Basic Python | Comfortable with ML basics |
| Live mentor sessions | One-to-one | ||
| Real dataset projects | |||
| Full end-to-end project | |||
| Best for | First steps in AI | Steady hands-on growth | A strong portfolio piece |
Shared Across Every Track
Privacy & Data Care
Learner data is handled carefully and only used to support your progress through the course.
Consistent Quality Checks
Course material is reviewed regularly to keep techniques and tools current.
Responsive Support
Questions are answered by people familiar with the specific track you are taking.
Documented Process
Every project follows a clear, repeatable process from brief to completion record.
Transparent Pricing by Track
AI Starter Lab Course
- Recorded lessons
- Practice notebooks
- Community access
Machine Learning Experiment Track
- Live mentor sessions
- Real project briefs
- Developing portfolio
Applied AI Research Studio
- One-to-one mentoring
- Detailed walkthroughs
- Completion record
Not Sure Which Track to Pick?
Tell us a little about your background and goals, and we will point you toward the right starting point.
Request a Quote