ML — The classical craft — rows, columns, and not fooling yourself

Quieter than the AI headlines and still what most companies actually need. A table, a column you want to predict, and the discipline to know whether it worked. Boosted trees have not stopped winning just because language models arrived.

Who this is for: Your data lives in a database, not in documents.

2 tracks · 15 hours · one of three paths in the learning roadmap by K Laxman. An optional 4-hour prelude, Just enough to not be bluffing, covers the underlying ideas first.

Track 4 — Classic ML

Still what wins on the data most companies actually have (~10 hours)

The half of the job the AI headlines skipped, and the half that quietly pays for most ML salaries: a table of rows, a column you want to predict, and the discipline not to fool yourself about how well it worked. Boosted trees did not stop winning on tabular data just because language models arrived — if your data lives in a database rather than in documents, this is the work.

Supervised learning, end to end

You can go from a CSV to a model with an honest score, and say what the score means.

train/validation/test · baselines · choosing the metric · cross-validation · over- and underfitting

Build this — A model with a baseline you have to beat. Pick any tabular dataset. Write down the dumbest possible predictor first — the mean, the majority class — and score it. Then beat it, and report both numbers. If you cannot beat the dumb one, that is the finding.

You're done when: You can say why a 99%-accurate model can be worthless, without looking it up.

Put to work in: Driver Drowsiness Detection

Leakage, imbalance, and the ways you fool yourself

You can spot the mistakes that make a model look brilliant offline and useless in production.

data leakage · time-based splits · class imbalance · distribution shift · error analysis

There is no single video for this one, and that is telling: it is the part of ML that gets learned by being burned. The two habits that matter — split on time whenever the real system will predict the future, and look at fifty actual errors before you touch the model — will save you more than any algorithm choice.

Build this — Break your own model on purpose. Take the model from the previous stage and introduce a leaked feature deliberately. Watch the score jump. That jump is exactly what a subtle leak looks like, and now you know its shape.

You're done when: Given a suspiciously good result, leakage is the first thing you check — and you know where to look.

Trees, boosting — and when to reach for deep learning

You choose a model class on the shape of the data, not on what is fashionable.

decision trees · random forests · gradient boosting · when deep learning wins · when it does not

Build this — Same dataset, three model classes. Boosted trees, a linear model, and a small neural net on identical splits. Report all three, plus training time. The answer on tabular data will usually surprise anyone who only reads AI news.

You're done when: Asked whether to use deep learning, your first question is about the data, not the model.

Put to work in: Table Recognition System

Track 4 — Evals, tracing & observability

The stage everyone skips (~5 hours)

This is the one that separates a demo from a product. As a system gets complicated, evals and tracing stop being nice-to-have and become the only way you can change anything without breaking it. If you cannot state your system's accuracy as a number with a dataset behind it, you do not have a system — you have a vibe.

Dataset → evaluator → task → interpretation

You can build an evaluation harness before you build the feature.

datasets · evaluators · LLM-as-judge and its failure modes · tracing · regression detection

Build this — Put a number on the RAG bot. Add the eval set and tracing to what you built in Track 2. Report the score in the README. Then change the chunk size and report it again — that delta is the whole discipline.

You're done when: You can say what your system's accuracy is, with a number and a dataset behind it.

Put to work in: CAMS Lens — grounded answers that have to be right

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