Learning with not Enough Data Part 1: Semi-Supervised Learning

1. 1Identify supervised learning tasks with limited labeled data
2. 2Explore semi-supervised learning as an alternative approach
3. 3Collect a large amount of unlabeled data to supplement the limited labeled data
4. 4Implement semi-supervised learning algorithms to improve model performance

Data scientists and machine learning engineers on a team can benefit from semi-supervised learning to improve model performance with limited labeled data, and product managers can understand the potential of this approach to reduce data collection costs

💡 Semi-supervised learning can improve model performance when labeled data is limited

🤖 Semi-supervised learning: using unlabeled data to boost model performance when labels are scarce!