Classic ML — Still what wins on the data most companies actually have
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.
Track 4 of the AI engineering roadmap by K Laxman — about 10 hours across 3 stages.
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
- scikit-learn User Guide — scikit-learn (docs, free, ~6h, rated 9/10). Not a tutorial — a reference written by people who had to make these algorithms actually work. Read the section for whatever you are using, every time, until you stop needing to.
- StatQuest — Josh Starmer (channel, free, ~4h, rated 9/10). The clearest explanations of the classical algorithms anywhere, and short enough to watch one the night before you need it. Reach for it whenever a term in the docs above doesn't land.
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
- Practical Deep Learning for Coders — fast.ai · Jeremy Howard (course, free, ~12h, rated 9/10). Top-down: you train a working model in lesson one and only then peel back the layers. If you want the neural-network half of ML properly — beyond the Track 0 pictures — this is still the best on-ramp there is. Take it as far as you need and no further.
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
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