There were days when organizations were striving for getting real time information. With mastering real time information, organizations were able to make decisions quickly. But with recent advances in analytics, cloud computing and data mining, the quest has transferred from real time information to real time ‘Sense and Response’.
In the race of real time response, Machine learning is a hot concept. Machine learning basically gives the computers the ability to learn without being explicitly programmed. Machine learning is a part of Artificial Intelligence which is broadly a mix of multiple disciplines like philosophy, psychology, information theory, control theory, data science and Neuro science. The uniqueness of machine learning is its ability to adapt to a changing environment. In machine learning, computers are provided the learning algorithm. With these learning algorithms, computers try to capture the general manual behavior of user to respond to any new data.
Machine Learning - Process:
Machine learning is a two phased process: Training and Application. In training phase, learning algorithm automatically prepares a model from the user general behavior. In application phase, the system uses the model to sense and respond to new data. The popular learning algorithm approaches are decision tree, artificial neural network, inductive logic programming, instance-based learning, reinforcement learning etc. Machine learning has found an increasing level of applicability in real world scenarios. At present, users are experiencing machine learning in real time applications such as Google Maps, Kinect, Netflix, iPhoto and Siri - a natural language processing.
Application of Machine Learning:
Considering its wide scope and applicability, organizations are continuously working on finding the solution for online learning. After a long drive, the world now is able to come up with real time inference but far behind the real time response. Machine learning field requires a human resource rich in analytic ability, statistics and domain knowledge. Machine learning have many applications such as market segmentation, customer lifetime value, predictive inventory planning, condition monitoring, credit worthiness estimation, risk analytics to name a few. To reap the fruits of ever increasing data, organizations needs to evolve highly sophisticated algorithm for real time learning and response.