Fortunately, for some classification tasks, you dont need to label all your training examples.
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Examples of unsupervised learning includecustomer segmentation, anomaly detection in web link traffic, and content recommendation.
Semi-supervised learning stands somewhere between the two.
It solves classification problems, which means youll ultimately need a supervised learning algorithm for the task.
Clustering algorithms are unsupervised machine learning techniques that group data together based on their similarities.
The clustering model will help us find the most relevant samples in our data set.
We can then label those and use them to train our supervised machine learning model for the classification task.
K-means is a fast and efficient unsupervised learning algorithm, which means it doesnt require any labels.
K-means calculates the similarity between our samples by measuring the distance between their features.
K-means clustering is a machine learning algorithm that arranges unlabeled data points around a specific number of clusters.
When training the k-means model, you must specify how many clusters you want to divide your data into.
But bear in mind that some digits can be drawn in different ways.
After training the k-means model, our data will be divided into 50 clusters.
This leaves us with 50 images of handwritten digits.
Training a machine learning model on 50 examples instead of thousands of images might sound like a terrible idea.
In contrast, training the model on 50 randomly selected samples results in 80-85-percent accuracy.
But we can still get more out of our semi-supervised learning system.
Using this method, we can annotate thousands of training examples with a few lines of code.
This will further improve the performance of our machine learning model.
S3VM is a complicated technique and beyond the scope of this article.
you’re free to then use the complete data set to train an new model.
The limits of semi-supervised machine learning
Semi-supervised learning is not applicable to all supervised learning tasks.
Semi-supervised learning is a brilliant technique that can come handy if you know when to use it.
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