Facts about Machine learning technology according to Lee Bressler

A technological guru Lee Bressler suggested that in today’s era machine learning technology allows computers to communicate readily with humans and it is also used to diagnose serious medical conditions and this machine learning technology is used in countless number of application. The main focus of Lee Bressler is towards artificial intelligence as well as machine learning technology. As a financial investor he further suggested that the usage of cloud computing in financial industry in the coming year is of great value. He further recommended that the emergence of Machine learning is from or before 1950s.

After 1950, continuously advances in machine learning is started developing. Among the three decades keeping in view about machine learning technology, Lee Bressler suggested that pattern recognition and a map routing algorithms were developed. In the year 1990, machine learning is switched from knowledge driven data to data driven which includes an algorithms that are designed to analyze a vast quantity of data before making any conclusions. In 1997, another huge leap forward for machine learning is developed.

As a technologist, Lee Bressler says that machine learning is one of the application of artificial intelligence that provides an ability to a system to automatically learn without being explicitly programmed. The main focus of machine learning is towards the development of a computer program that can access data and use it to learn for themselves. He further suggested that a machine learning process begins with the observation of data. The main goal of machine learning is to allow the computers to learn automatically with any human involvement.

In addition to machine learning technology Lee Bressler further says that machine learning is often categorized as supervised and unsupervised learning algorithm. The supervised learning data analyze the past data and focus in predicting the future events. This learning algorithm also compare its output with the exact output and find error in order to modify the model accordingly whereas in unsupervised learning algorithm used to predict the output from unclassified data. The main focus of unsupervised learning is to know that how systems can infer a function to describe a hidden structure from unlabeled data.