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Artificial Intelligence Foundations: Machine Learning

Beginner 2 hours English Course Certificate
SkillsMachine learningML lifecycleData preparationModel trainingModel evaluationAI foundations

Is this course right for you?

Our take
This LinkedIn Learning course explains how machine-learning systems find patterns and make decisions, then puts you to work training your first model.

Good for: A gentle first hands-on taste of machine learning.

Skip if: You want rigorous ML theory or a coding-heavy course.

It walks the full lifecycle at a gentle pace — preparing data, choosing an algorithm, evaluating the result — so you finish with a real, if introductory, sense of what doing ML actually involves rather than just what it is. That gentleness sets the fit: it's a good bridge from curiosity to a first hands-on taste, and the wrong course if you want rigorous ML theory or a coding-heavy treatment.

It comes with a LinkedIn Learning subscription ($29.99/month, or $19.99 billed annually, usually a free trial; free through many libraries), with a completion certificate for your profile that records the learning, not a qualification. The foundations it covers stay relevant even as tools change (as of 2026).

Comparison · LBS

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About this course

Artificial Intelligence Foundations: Machine Learning explains how ML systems find patterns and make decisions, then has you train your first model. It walks through the full lifecycle — data preparation, choosing an algorithm, evaluating a model, and building pipelines — across supervised, unsupervised and reinforcement learning.

Browse all AI & ML courses Browse all LinkedIn Learning courses

Instructor

KW
Kesha Williams
LinkedIn Learning instructor

Taught by Kesha Williams, an award-winning technology leader and AWS Machine Learning Hero, known for making ML approachable.

Frequently asked questions

No — it is a conceptual foundations course explaining what machine learning is, the main types (supervised, unsupervised), how models learn from data, and common applications, without hands-on coding. From LinkedIn Learning, it builds understanding and vocabulary rather than practical model-building skills. If you want to actually build ML models, you would take a hands-on Python course; this is the conceptual grounding first.

No. Because it is conceptual rather than hands-on, no programming is required — it explains machine-learning ideas in accessible terms for a general audience. Anyone comfortable with technology can follow it. It suits people wanting to understand what machine learning is and how it works, including non-technical professionals, rather than those seeking to write ML code, which needs a different, hands-on course.

It is short — typically an hour or two — as a focused foundations course. That makes it a quick, low-commitment way to build a solid conceptual understanding of machine learning, after which you could pursue hands-on, code-based courses if you want to actually build models. It is orientation, giving you the concepts before any technical practice.

Not by default — it runs on LinkedIn Learning, which is subscription-based — but a free trial is common, and many public libraries and universities give members full access at no cost. Check whether a library card or an institution you belong to already provides LinkedIn Learning before paying, since that quietly unlocks this course and the whole catalogue.

Yes, you earn a LinkedIn Learning completion certificate for your profile. For a conceptual foundations course, treat the certificate as a marker that you understand the basics rather than a meaningful technical credential — machine-learning roles hire on demonstrable, hands-on skill. The genuine value is understanding what machine learning is and how it works, as a foundation for further study.
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