If I had to pick one of the two MathWorks Onramps for someone to do first, it would be this one, which may be an unpopular position given deep learning gets all the attention. The reason is that this course spends its time on the part of machine learning that beginners routinely get wrong. The module list is telling. Importing data, extracting features from real tabular data, partitioning into training and test sets, training a classifier, adjusting hyperparameters, evaluating with a confusion matrix, and then strategies for improving performance.
That is the actual shape of a supervised learning project. Compare it against the standard introductory experience where somebody fits a model on a clean dataset, reports 97 percent accuracy, and never learns why that number is meaningless without knowing the class balance and whether the test set was ever touched during tuning. Teaching partitioning and confusion matrices as first-class topics rather than a footnote is the right call and it is the habit that carries over to any language or library. Feature extraction gets proper attention too, which I appreciate.
Deep learning has made it fashionable to treat feature engineering as a solved problem you no longer need to think about, and for images and text that is largely true. For the tabular and sensor data that most engineers actually have, it remains the single highest-leverage thing you do, and a course that makes you extract features from a real signal rather than handing you a tidy matrix is teaching something useful. The Classification Learner app is the double-edged part. Being able to train a dozen model families side by side and compare them visually is a genuinely good way to build intuition about which algorithms suit which data shapes, and you learn more in ten minutes of that than in an hour of reading about it.
The risk is that it makes model selection feel like pressing buttons until a number goes up. The course does not really confront that risk, and a note about why the best validation score is not automatically the model you should ship would have improved it. Same structural caveats as the deep learning Onramp. It is free, it runs in the browser with no install, exercises give automated feedback, the certificate costs nothing, and every line of it is MATLAB.
If scikit-learn is where you are heading, the concepts transfer and the code does not. If you already work in MATLAB, this is two hours extremely well spent, and it slots straight into whatever engineering work you are already doing. Scope is limited to classification. No regression, no clustering, no dimensionality reduction.
Two hours also means breadth over depth throughout. Three point nine. Slightly below its deep learning sibling only because the app-driven approach makes it a little easier to finish without having understood much, but the workflow discipline it teaches is the better foundation.