Connect data preparation and practical prediction projects with careful comparison of model fit and forecast limits.
Use a sequence of applied examples to connect data preparation, a supervised classification project, clustering, and time-series forecasting. The course focuses on decisions that make a result interpretable: define the prediction target, inspect and prepare data without leaking future information, compare a result with a suitable baseline, and describe what the model cannot establish. The original activities use safe, public or synthetic data and emphasize reproducibility, uncertainty, and responsible communication rather than treating a tutorial result as general evidence.
Create a data-preparation plan that records missing values, encoding choices, target definition, and train/test separation. Explain how a supervised prediction workflow differs from unsupervised clustering and forecasting. Evaluate a project result against a baseline and identify evidence needed before applying it to a new population or time period.
Basic familiarity with Python, tables, and introductory machine-learning vocabulary is recommended. The assignments can be completed as written analyses without running code. Use public or synthetic data, do not place personal information in a notebook, and do not treat a course project as evidence for a consequential decision.
Learners with basic familiarity who are ready to build practical, independent skills
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 · Intermediate · Full course.
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