B.E in Artificial Learning is an undergraduate programme with advanced learning solutions imparting knowledge of advanced innovations like machine learning, often called deep learning and artificial intelligence.

This specialisation is designed to enable students to build intelligent machines, software, or applications with a cutting-edge combination of machine learning, analytics and visualisation technologies. The main goal of artificial intelligence (AI) and machine learning is to program computers to use example data or experience to solve a given problem. Many successful applications based on machine learning exist already, including systems that analyze past sales data to predict customer behaviour (financial management), recognize faces or spoken speech, optimize robot behaviour so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.

Artificial Intelligence and Machine Learning is currently the hot topic and probably the most seeked out discipline in both Industry and Academics. Most of the IT companies like Microsoft, Google, AMAZON, Tesla, NVIDIA many more have adapted to this concept in their system.Artificial Intelligence software, by the leading artificial intelligence companies, is a fast-growing market within computer science that focuses on creating and providing intelligent problem-solving solutions. A.I. software can include speech recognition, problem-solving, learning and planning that can aid or even replace human engagement in a process.

Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.Google CEO SundarPichai says artificial intelligence is going to have a bigger impact on the world than some of the most ubiquitous innovations in history.Tesla is using advanced AI for vision and planning, supported by efficient use of inference hardware to achieve a general solution to full self-driving.