ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

Artificial Intelligence and Machine Learning Made Simple
Artificial Intelligence is a system or a product that can work like a smart human without human intervention. It is the latest technology that can be applied in multi discipline. Wherever machine can under a human voice, symbol, expression and feelings AI is successful. Around the globe AI will be implemented in almost every field by 2030. The revolutionary technology that will take our research and innovation to the next stage. If artificial intelligence is our future. So a deep study is initiated to the young minds through our innovative undergraduate level course, where lots of case studies and technical knowledge will be taught to the students. Taking a big leap in future through this UG course will lead the student ahead of other people who are still updating to the future technologies.

What is Machine Learning?
Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behavior exists in the past, then you may predict if or it can happen again. Means if there are no past cases then there is no prediction. ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.

There are 3 major areas of ML:

Supervised Learning
In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyse the data and produce an inferred function. The correct solution thus produced can be used for mapping new examples. Credit card fraud detection is one of the examples of Supervised Learning algorithm.

Unsupervised Learning
Unsupervised Learning algorithms are much harder because the data to be fed is unclustered instead of datasets. Here the goal is to have the machine learn on its own without any supervision. The correct solution of any problem is not provided. The algorithm itself finds the patterns in the data. One of the examples of supervised learning is recommendation engines which are there on all e-commerce sites or also on Facebook friend request suggestion mechanism.

Reinforcement Learning
This type of Machine Learning algorithms allows software agents and machines to automatically determine the ideal behaviour within a specific context, to maximise its performance. Reinforcement learning is defined by characterising a learning problem and not by characterising learning methods. Any method which is well suited to solve the problem, we consider it to be the reinforcement learning method. Reinforcement learning assumes that a software agent i.e. a robot, or a computer program or a bot, connect with a dynamic environment to attain a definite goal. This technique selects the action that would give expected output efficiently and rapidly. Artificial Intelligence and Machine Learning always interests and surprises us with their innovations. AI and Ml have reached industries like Customer Service, E-commerce, Finance and where not. By 2020, 85% of the customer interactions will be managed without a human. There are certain implications of AI and ML to incorporate data analysis like Descriptive analytics, Prescriptive analytics and Predictive analytics.