Data Science with Python: Foundations of Machine Learning

It walks the full loop once — identifying the right problem, training a model on a prepared dataset, and evaluating it — which is the arc that turns 'I've heard of ML' into 'I've done it once end to end'. The emphasis on framing the problem first is what sets it apart from courses that jump straight to algorithms, and it's the right instinct for beginners.

Because it's gentle and foundational, it's redundant if you already know the ML basics or you want deep theory. It's on 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).

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. It also frames when machine learning is the wrong tool for a problem, so you can steer a project toward a simpler solution before sinking time into a model.