Meticlux

Here are some definitions for commonly used terms/technologies in machine learning. I’ll try to update and improve this page with new entries over time. Apache Spark — A library for distributed computing for large-scale data manipulation and machine learning. Artificial Neural Networks — Machine learning algorithms inspired by biological neural networks. Back-propagation — An algorithm for training neural networks in which errors are propagated backwards through the network. Big Data — Data which is difficult to work upon using a single machine, typically in the order of terabytes or more. It can also mean machine learning and other types of analyses on data of this scale. Classification — A machine learning problem involving the prediction of two or more classes from an observation. Clustering — The process of grouping observations that are similar according to a particular criterion. Cython — A Python-like language uses to give C-like performance to Python. Cross Validation — ...

“Learning is any process by which a system improves performance from experience” Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning is concerned with computer programs that automatically improve their performance through experience. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Why Machine Learning ? •Develop Systems that can automatically adapt and customize themselves to individual users. •Discover Knowledge from Large Database (Data Mining) •Supporting many Industries. •Increasing Computational Power. Who’s using Machine Learning? In today’s everyone wants to switch to machine learning. Government Agencies Financial Services Health Care Industry Retails ( Website Recommending Items) Transportation Industry Defense & Security IT Companies