Unlike supervised learning, unsupervised machine learning doesn’t require labeled data. It peruses through the training examples and divides them into clusters based on their shared characteristics.

Unsupervised learning excels in domains for which a lack of labeled data exists, but it’s not without its own weaknesses — nor is semi-supervised learning.

Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow Despite the success of supervised machine learning and ...

To a large extent, supervised ML is for domains where automated machine learning does not perform well enough. Scientists add supervision to bring the performance up to an acceptable level.

Semi-supervised learning combines supervised and unsupervised learning for efficient data analysis. This hybrid approach enhances pattern recognition from large, mixed data sets, saving time and ...

This is part two of my series based on Lomit Patel’s “Lean AI” (O’Reilly, ISBN:978-1-492-05931-8). The first discussed business applications can benefit from supervised learning. This ...

As mentioned, semi-supervised learning bridges the gap between supervised and unsupervised learning, utilising labelled and unlabelled data together. But within this broad category, ...

Before we dive into supervised and unsupervised learning, let’s have a zoomed-out overview of what machine learning is. In their simplest form, today’s AI systems transform inputs into outputs.