Build an intuitive foundation in machine learning tasks, model concepts, and responsible data splits.
A four-lesson entry path from the basic idea of machine learning to learning-task categories, model concepts, and data splitting. Learners compare supervised, unsupervised, and reinforcement-learning examples, then examine why training and evaluation data must be separated carefully. The final activity applies these ideas to a small, permissioned dataset; no programming background is required.
Describe machine learning as finding patterns in examples and distinguish it from a fixed hand-written rule. Classify simple scenarios as supervised, unsupervised, or reinforcement learning and explain what evidence each uses. Identify features, a target, training data, and evaluation data in a basic prediction task. Propose a data split that reduces leakage and explain one limitation of the resulting evaluation.
No programming or statistics experience is required. Familiarity with rows and columns is helpful; use only public or otherwise permitted practice data.
Beginners who want a clear, guided introduction without unnecessary jargon
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Beginner · Full course.
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