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AI/ML for Life Sciences
An applied machine-learning track for biological data: problem framing, leakage and splits, evaluation you can defend, interpretability, and deployment under review.
An applied machine-learning track for biological data: problem framing, leakage and splits, evaluation you can defend, interpretability, and deployment under review.
This track runs on milestones you submit, not videos you watch. Each milestone names what you hand in, and the review is what turns it into a verified step on your record. Where an existing course or deep-dive guide already covers part of the track, it is linked from this page.
Tools the work uses
scikit-learnThe baseline every complex model has to beat.
PyTorchFor when a learned representation is genuinely needed.
pandas with MatplotlibAuditing the data before modelling it.
SHAP or attention analysisEvidence about why the model answered the way it did.
Where this track leads
- Machine-learning engineer for biology
- Computational biologist
- Data scientist in healthcare
- AI and biology research scientist
Where this track stands today
Nothing here is padded out to look finished. This is the honest state of the parts that are still being built.
Milestone briefs (14 weeks)Being written
Machine Learning for BiotechLive in the catalogue
02/Other tracks
Start with the milestone, not the video.
Enrol once and every track on the platform becomes available — the milestones, the review and the certificate at the end.