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Understanding Machine Learning with Python 3

Beginner 1.9 hours English
SkillsMachine learningPythonData preparationModel trainingModel evaluationscikit-learn

Is this course right for you?

Our take
A Pluralsight course that runs you through one complete machine-learning workflow on a real prediction problem, start to finish.

Good for: A concise, hands-on first pass at the end-to-end ML workflow.

Skip if: You want deep ML theory or you cannot code Python.

In roughly two hours you prepare data, pick an algorithm, train a model and check how well it did — the whole loop once, concretely, rather than a survey of theory. That's genuinely useful as a first hands-on pass if you can already code a little Python, and the wrong choice if you want deep ML theory or you don't code yet. Access comes with a Pluralsight subscription (Standard about $29/month or $299/year, Premium about $45/month or $499/year, 10-day trial). The workflow it teaches is the same one practitioners still follow (as of 2026).

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About this course

Understanding Machine Learning with Python 3 takes you through a complete ML workflow on a real prediction problem. In about two hours it covers preparing data, selecting an algorithm, training a model and evaluating its performance — all in Python 3 using the scikit-learn library inside the Jupyter Notebook environment.

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Instructor

JK
Jerry Kurata
Pluralsight instructor

Taught by Jerry Kurata, an experienced technologist and Pluralsight author specialising in machine learning and data topics.

Frequently asked questions

Yes. As the title says, it teaches machine learning using Python, so you should already be comfortable with Python basics — variables, functions, working with libraries — before starting. It is not a Python primer. You do not need prior machine-learning knowledge, which it introduces, but arriving without working Python means struggling with the code rather than focusing on the ML concepts.

An introduction to practical machine learning in Python: the core workflow of preparing data, training models, and evaluating them, typically using scikit-learn, across common tasks like classification and regression. It aims to give you a working understanding of how to build and assess basic models rather than deep theory or advanced techniques, so it suits people getting started with applied ML.

Yes. Pluralsight offers a free trial (commonly around ten days), enough to work through a focused course like this at no cost if you are disciplined. Beyond the trial, Pluralsight is a subscription, so ongoing access to this and its wider library is paid — the trial is the way to sample it before committing to a subscription.

Pluralsight issues a course completion record rather than an industry certification. In machine learning, what matters to employers is demonstrable skill — models you have built and projects you can explain — far more than a completion badge. Treat the record as a marker of learning; the real value is the practical ML understanding you take from actually doing the work.

It is a focused course — a few hours — so most people finish it over a sitting or two, faster given the assumed Python background. It is an introduction rather than a comprehensive program, so treat it as a starting point in applied machine learning that you build on with deeper courses and your own projects afterward.
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