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Applied Machine Learning

Use fictional depot jobs for supervised prediction, training-only preprocessing, validation, frozen policies and a future holdout. Preserve the reference no-go decision when alerts exceed operational capacity. No deployment success or real-world impact is claimed.

Preparation: Python for AI and Data, SQL for Data Analysis and AI, and Statistics with Data Storytelling, or equivalent table, Python and uncertainty skills.

Read all 12 modules as a guest, in any order. An optional account saves your place and reading marks. Practice rubrics are for self-review; they do not unlock reading or award a certificate.

Planning estimate: See the provisional estimates in the syllabus; learner pacing has not been piloted. Timing and learner outcomes have not been piloted.

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12 lessons remain open to guests. Marking a lesson read records reading only; it does not award assessment credit or a certificate.

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Course contents

Practice and references

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Applied Machine Learning

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Course contents

Start with a prediction-time contract

Make every label and feature earn its place

Choose a split that resembles the future

Treat preprocessing as part of the learned model

Compare regression rules and inspect their mistakes

Read classification metrics as counts and questions

Cross-validation, tuning and test lockdown

Calibration and threshold tradeoffs

Error analysis and interpretation limits

Reproducibility, handoff and drift

Follow one complete prediction investigation

Capstone: deliver an auditable intake prediction study

Practice and references