Semi-Supervised Learning Techniques
Explore semi-supervised learning methods that use both labeled and unlabeled data to enhance model accuracy. Understand key assumptions, the role of pseudo-labeling, and applications in text classification and bioinformatics. This lesson helps you apply semi-supervised learning effectively in machine learning projects.
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Semi-supervised learning
Supervised learning involves the usage of labelled data, and unsupervised learning works without labelled data. Semi-supervised learning lies between the two fields. It makes use of both labelled and unlabelled datasets. We learned about the technique of pseudo-labeling in the initial lesson of this chapter.
Labeling data is a costly process. The biggest benefit of semi-supervised learning is that it requires a small amount of labelled data. In most ...