In the majority of supervised learning applications, the ultimate goal is to develop a finely tuned predictor function h(x) (sometimes called the “hypothesis”). Semi-supervised learning falls in between unsupervised and supervised learning. Machine learning is an application of artificial intelligence that uses statistical techniques to enable computers to learn and make decisions without being explicitly programmed. It is predicated on the notion that computers can learn from data, spot patterns, and make judgments with little assistance from humans. Another important decision when training a machine-learning model is which data to train the model on. For example, if you were trying to build a model to predict whether a piece of fruit was rotten you would need more information than simply how long it had been since the fruit was picked.
This is like letting a dog smell tons of different objects and sorting them into groups with similar smells. Unsupervised techniques aren’t as popular because they have less obvious applications. Sometimes … Read MoreView More t-Distributed Stochastic Neighbor Embedding (t-SNE)