Semi-supervised Learning

When a new product is being proposed, or a new industry has come around, a common problem they face is a lack of labeled training data to apply traditional supervised learning approaches

• Insufficient Quantity of Labeled Data

• Insufficient Domain Expertise to Label Data

• Insufficient Time to Label and Prepare Data

• High-Quality Labeled Data

When working in a field that deals with fast evolving problems, collecting and preparing a dataset to build a useful solution quickly enough may be impractical

• Insufficient Quantity of Labeled Data

• Insufficient Domain Expertise to Label Data

• Insufficient Time to Label and Prepare Data

• High-Quality Labeled Data

It's known that 60% or more of the time spent working on machine learning problems is dedicated to the preparation of a dataset.

• Insufficient Quantity of Labeled Data

• Insufficient Domain Expertise to Label Data

• Insufficient Time to Label and Prepare Data

• High-Quality Labeled Data

Getting a domain expert to label training data can quickly become expensive, hence it's often not a viable solution

• Insufficient Quantity of Labeled Data

• Insufficient Domain Expertise to Label Data

• Insufficient Time to Label and Prepare Data

• High-Quality Labeled Data

Some problems can be labeled by absolutely anyone

• Insufficient Quantity of Labeled Data

• Insufficient Domain Expertise to Label Data

• Insufficient Time to Label and Prepare Data

• High-Quality Labeled Data

The demand for high-quality labeled data often leads to a major roadblock when businesses attempt to approach problems using machine learning

• Insufficient Quantity of Labeled Data

• Insufficient Domain Expertise to Label Data

• Insufficient Time to Label and Prepare Data

• The Data Problem

Usually, data scientists would obtain more data but the issue in this scenario is that do SO may be impractical, expensive, or impossible without waiting for time to pass so data can be accumulated.

• Insufficient Quantity of Labeled Data

• Insufficient Domain Expertise to Label Data

• Insufficient Time to Label and Prepare Data

• High-Quality Labeled Data

A type of machine learning that uses a combination of a labeled data and unlabeled data to train models.

To build a model that can make accurate prediction to the new data, unseen data.

The model is trained by both label and unlabeled data to generalize well to any future data points.