Semi-supervised Machine Learning
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Semi-supervised machine learning is a technique that uses both labeled and unlabeled data for training. It begins by taking a large unlabeled dataset and labeling a small portion of it. Then it uses unsupervised learning to cluster the unlabeled data and supervised learning on the labeled data to classify the remaining unlabeled data. Some applications of semi-supervised learning include internet content classification, where it is not feasible to label all webpages, and audio/video analysis, where the volume of files makes complete labeling impossible.
Semi-supervised Machine Learning
- 1. Semi-supervised Machine Learning Spotle.ai Study Material Spotle.ai/Learn
- 2. Spotle.ai Study Material Spotle.ai/Learn Recap - Supervised Machine Learning Supervised learning is a learning in which we teach or train the machine using data which are properly or rather correctly labeled. Training data Machine learning algorithm Predictive model Model evaluation Feedback loop Labeled
- 3. Spotle.ai Study Material Spotle.ai/Learn Recap - Unsupervised Machine Learning Input data Machine learning algorithm Outputl Unlabeled Unsupervised learning is the learning of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.
- 4. The biggest drawbacks Supervised Machine Learning algorithm needs labeled data from which it will learn. We have an enormous amount of data available in the world, including texts, images, audios, videos, time-series and many more, but only a small fraction such data is actually labeled. This process of labeling data, whether algorithmically or by hand, is a very costly process, especially when we are dealing with large volumes of data. On the other hand, the biggest disadvantage of any Unsupervised Machine Learning is that it’s application spectrum is limited.
- 5. Spotle.ai Study Material Spotle.ai/Learn Problems! OK. How to get rid of them? How about using the benefits of unsupervised learning and building clusters using the dataset? Unsupervised learning doesn’t work on the labeled data. So the cost of labeling the data is saved. And then using the benefits of supervised learning by using a small amount of labeled dataset and classifying the dataset as accurately as possible? We can do that. This technique is called semi-supervised machine learning. Supervised Learning Unsupervised Learning
- 6. Semi-supervised Machine Learning A. Pick-up the large unlabeled dataset. A. Label a small portion of the dataset. A. Put the unlabeled dataset into clusters using Unsupervised Machine Learning algorithm. A. Build your model to use the labeled data to label and classify the rest of the unlabeled data.
- 7. Spotle.ai Study Material Spotle.ai/Learn Some applications Internet Content Classification: There are millions and millions of webpages. It is practically impossible to label all the webpages if you need to. Semi supervised machine learning helps here to classify the webpages. Audio/ Video analysis: We have an overwhelming amount of audio and video files all over. Labeling them is a massive task, if not unfeasible. Semi supervised learning comes handy in audio/ video analysis.