A Tour of Machine Learning Algorithms

Let's take a look at three different learning styles in machine learning algorithms: Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. Example problems are clustering, dimensionality reduction and association rule learning. They are concerned with building much larger and more complex neural networks and, as commented on above, many methods are concerned with semi-supervised learning problems where large datasets contain very little labeled data. Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an unsupervised manner or order to summarize or describe data using less information.