PLPluralsight

Building Machine Learning Models in Python with scikit-learn

4.5(126)

Beginner 3.2 hours English Professional Certificate

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.

The honest take: this course was last updated in April 2018. scikit-learn's core API has stayed fairly stable since then, so the concepts and code patterns mostly still apply, but expect minor syntax drift and don't expect coverage of newer scikit-learn features or modern MLOps practices.

What you'll learn

Process and prepare data for machine learning with scikit-learn

Apply Lasso and Ridge regression for regularized regression problems

Build classification models using Support Vector Machines

Perform unsupervised learning via clustering and dimensionality reduction

Understand the broader scikit-learn workflow for model building

This course includes

3.2h

On-demand video

Yes

Mobile access

English

Language

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Instructor

Janani Ravi

Pluralsight instructor

— learners— courses — instructor rating

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

Requirements

Basic Python programming knowledge — no prior ML experience required

Who this course is for

Engineers and data scientists new to scikit-learn
Python programmers wanting practical ML model-building skills

About this provider

Pluralsight

Tech skills platform for software developers, IT ops, and security professionals.

4.4 trust score

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Frequently asked questions

No — the course explicitly states no prior ML experience is needed, only basic Python knowledge.

Mostly yes — scikit-learn's core API (regression, classification, clustering) has remained fairly stable, though expect minor syntax drift and no coverage of newer features.

Lasso/Ridge regression, Support Vector Machine classification, and clustering/dimensionality reduction for unsupervised learning.

3 hours 13 minutes.

4.5★ from 126 ratings.