Fueled by the massive amount of research by companies, universities and governments around the globe, machine learning is a rapidly moving target. Breakthroughs in AI and ML seem to happen daily, rendering accepted practices obsolete almost as soon as they’re accepted. One thing that can be said with certainty about the future of machine learning is that it will continue to play a central role in the 21st century, transforming how work gets done and the way we live. Actions include cleaning and labeling the data; replacing incorrect or missing data; enhancing and augmenting data; reducing noise and removing ambiguity; anonymizing personal data; and splitting the data into training, test and validation sets. While a lot of public perception of artificial intelligence centers around job losses, this concern should probably be reframed. With every disruptive, new technology, we see that the market demand for specific job roles shifts.
Supervised learning algorithms can be further subdivided into regression and classification. In supervised tasks, we present the computer with a collection of labeled data points called a training set (for example a set of readouts from a system of train terminals and markers where they had delays in the last three months). … Read MoreView More Gaussian Mixture Models (GMM)