PLPluralsight
Data Science with Python: Foundations of Machine Learning
Beginner 0.7 hours English Professional Certificate
About this course
Amber Israelsen teaches the complete beginner workflow for solving business problems with Python and machine learning: identifying the right problem to solve, training a model with a prepared dataset, and evaluating how well it performs.
The honest take: at just 40 minutes and 4.7★ from only 20 ratings, this is a brisk conceptual walkthrough rather than a deep technical course — treat it as a fast on-ramp into the ML workflow before diving into a longer, library-specific course like scikit-learn.
What you'll learn
Frame a business problem as a machine learning problem
Prepare a dataset for model training
Train a basic machine learning model in Python
Evaluate model performance using standard metrics
Understand the end-to-end ML workflow from problem to evaluation
This course includes
0.7h
On-demand video
Yes
Mobile access
English
Language
Comparison · LBS
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Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.
Instructor
AI
Amber Israelsen
Pluralsight instructor
— learners— courses — instructor rating
Amber Israelsen is a Pluralsight author focused on practical, beginner-friendly data science and Python content.
Requirements
- Basic Python familiarity recommended
Who this course is for
- Python beginners curious about machine learning
- Analysts exploring a transition into data science
About this provider
PL
Pluralsight
Tech skills platform for software developers, IT ops, and security professionals.
4.4 trust score
Frequently asked questions
40 minutes — it's a brisk, high-level overview rather than an in-depth course.
Basic Python familiarity is recommended, though the course is pitched at beginners.
No — it covers the general workflow conceptually rather than diving deep into any one library.
No certificate — Pluralsight tracks course completion within your subscription.
It's based on only 20 ratings, so treat it as a light signal rather than a robust consensus.