If you’re new to machine learning, you may have come across terms like supervised, unsupervised, and semi-supervised learning. The first two are easy to understand. One has full guidance, the other has none. But what does semi-supervised mean?
Let’s break it down in a simple way.
Imagine you’re asked to sort fruits. Someone gives you the names of just five fruits, but you have a basket of 500. You use the five examples to guess the rest. This is exactly how semi-supervised learning works.
What Is Semi-Supervised Learning?
It is a technique where the model learns from a small amount of labelled data and a large amount of unlabelled data. This method saves both time and cost because labelling data is a long and boring task.
Why Use It?
Labelled data is limited – Creating it needs time and money.
Unlabelled data is easy to find – From websites, apps, social media, etc.
It gives better results than using unlabelled data alone.
Where Is It Used?
Spam filters (Gmail)
Voice recognition (Alexa, Google)
Medical scans (X-rays)
E-commerce suggestions (Amazon)
Face recognition (social media)
Simple Algorithms You Can Explore
Self-training
Co-training
Graph-based learning
Semi-supervised SVM
Generative models
Each has its own way of using both labelled and unlabelled data.
When Should You Try This?
Use semi-supervised learning when:
You have very little labelled data
You have large unlabelled datasets
You want decent performance without too much manual effort
Final Words
Semi-supervised learning is becoming popular in real-world machine learning projects, especially in India where start-ups and small businesses work with limited data. If you're learning ML or planning your first project, this is a great place to start.
Let us know if you're exploring this method—we’d love to hear your experience.
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