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Who it's for
Students
Graduate with proof, not just a degree.
Professionals
Be the one at work who actually knows AI.
Executives
Make the AI call with confidence.
Teams
Upskill the whole team, and measure it.
Educators
Teach what you know. Get paid fairly.
Platform
Pathfinder roadmaps
Your goal in, a week-by-week plan out.
Squad Mode
Study rooms with friends, a timer and a mood.
Verified Credentials
Proof you can do it, checkable in one click.
Teach & earn
Prove a topic, then get paid to teach it.
Ask the Library
Ask anything. Get the exact lesson.
Study tools
Flashcards, a notebook and a tutor that remember for you.
Resources
The library
Every course, module and topic.
AI of the Week
One development, explained.
Executive briefings
Twenty minutes on strategy and risk.
Paper Club
One paper a week, reproduced.
Office hours
AMAs, workshops, study halls.
Research Labs
Guided research, public showcase.
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Library
/
Machine Learning (Supervised + Unsupervised)
/
Supervised Learning by Building
topic
S01 路 Build: How Training Works (fit, loss, optimisation)
Parameters vs hyperparameters
15 min
Loss functions and what they reward
10 min
Optimisation: gradient descent, epochs and batches in practice
10 min
Learning curves: more data or a better model?
10 min
The scikit-learn API and how other libraries differ
10 min
Total: 0.9 h of applied study listed above.
S01 路 Build: How Training Works (fit, loss, optimisation) 路 1x1labs.ai