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Building Machine Learning Models in Python with scikit-learn

4.5(126) on Pluralsight
Beginner 3.2 hours English
Skillsscikit-learnMachine learningPythonRegressionClassificationModel building

Is this course right for you?

Our take
Janani Ravi's Pluralsight course is about the doing: constructing machine-learning models with scikit-learn.

Good for: Building ML models hands-on with scikit-learn.

Skip if: You cannot code Python or want deep learning.

It works through data processing, regression approaches like Lasso and Ridge, and classification — hands-on with the standard Python ML library, so you come away having actually built models rather than just understood them conceptually. It sits a notch past a first-ever ML course: what you gain is real model-building practice, and the regularisation methods (Lasso, Ridge) are exactly the kind of practical technique that separates a usable model from an overfit one.

It assumes you can code some Python, so it's not a starting point if you can't, and it stays with classic ML rather than deep learning. It's on a Pluralsight subscription (Standard about $29/month or $299/year, Premium about $45/month or $499/year, 10-day trial). scikit-learn is a stable, industry-standard library (as of 2026).

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About this course

Janani Ravi teaches how to construct machine learning models using scikit-learn, the widely-used Python ML library. The course covers data processing techniques, specialized regression approaches (Lasso and Ridge), classification methods including Support Vector Machines, and unsupervised learning through clustering and dimensionality reduction.

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Instructor

JR
Janani Ravi
Pluralsight instructor

Janani Ravi is a Pluralsight author and co-founder of Loonycorn, producing technical courses across data science, cloud, and software engineering.

Frequently asked questions

Yes. It teaches building machine-learning models with Python's scikit-learn library, so comfortable Python is essential, and some familiarity with data handling helps. It is not a Python primer. Arriving with solid Python lets you focus on the machine-learning concepts and the scikit-learn workflow rather than wrestling with syntax, which is the point of the course.

No. It focuses on classical machine learning with scikit-learn — models like regression, classification, and clustering — rather than deep learning and neural networks, which use different tools like TensorFlow or PyTorch. So if your goal is deep learning specifically, this is not it; it covers the broad, foundational ML techniques that scikit-learn provides, which are the right starting point for most ML work.

Building and evaluating machine-learning models in Python with scikit-learn: the workflow of preparing data, training models across common tasks like classification and regression, tuning, and assessing performance. It is hands-on and focused on applying scikit-learn correctly, so you come away able to build and evaluate standard ML models in Python rather than only understanding the theory.

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 it is a subscription, so ongoing access to this and Pluralsight's wider machine-learning library is paid — the trial lets you sample it before committing.

Pluralsight issues a course completion record rather than an industry certification. In machine learning, what matters is demonstrable work — models and projects you can show. Treat the completion as a marker of learning; use the course to genuinely build scikit-learn models, since a portfolio of real work carries far more weight than the record itself.
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