Expectation-Maximisation — When Missing Labels Are Just Missing Data

1. 1Implement Expectation-Maximisation algorithm in Python to handle missing labels
2. 2Use the algorithm to estimate model parameters and hidden variables
3. 3Apply the technique to semi-supervised learning tasks, such as image or text classification
4. 4Evaluate the performance of the model using metrics like accuracy and F1-score
5. 5Compare the results with supervised learning approaches to assess the benefits of Expectation-Maximisation

Data scientists and machine learning engineers can benefit from this technique to improve model performance when labeled data is scarce

💡 Expectation-Maximisation can be used to handle missing labels by treating them as missing data, allowing for more efficient use of available data

🤖 Handle missing labels with Expectation-Maximisation! 📊 Improve model performance in semi-supervised learning tasks #machinelearning #semisupervisedlearning