Meaning of Semi-Supervised Learning

Simple definition

A type of machine learning that uses both labeled and unlabeled data, typically with more unlabeled data.

How to use Semi-Supervised Learning in a professional context

Used in image recognition when labeling data is expensive, but unlabeled data is abundant.

Concrete example of Semi-Supervised Learning

A company trains a model to classify product images using a small number of labeled images and many unlabeled ones.

Why is semi-supervised learning useful?

It allows models to be trained with fewer labeled data, saving time and resources.

What types of problems benefit from semi-supervised learning?

When labeled data is scarce but unlabeled data is plentiful, such as in medical imaging.

How does semi-supervised learning compare to unsupervised learning?

It uses some labeled data, while unsupervised learning uses only unlabeled data.
Related Blog articles
Navigating Japan’s tech job market in 2025–2026: meet our new Career Coach!

Navigating Japan’s tech job market in 2025–2026: meet our new Career Coach!

Paul Roberts, a seasoned tech recruiter who is working closely with engineers and hiring teams...

Financing your bootcamp in Canada: Spotlight on Windmill Microlending

Financing your bootcamp in Canada: Spotlight on Windmill Microlending

At Le Wagon Montréal, we know that investing in a bootcamp is a big step....

Why the Data Analytics bootcamp was an asset for Coline

Why the Data Analytics bootcamp was an asset for Coline

After just two months in Le Wagon Montréal’s Data Analytics bootcamp, Coline walked away with...

    Suscribe to our newsletter

    Receive a monthly newsletter with personalized tech tips.