Astonishing Hierarchy of Machine Learning Needs – Machine Learning entirely depend upon algorithms of two kinds Learning style Symmetry & similarity Machine Learning is hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is […]
Reinforcement learning can be understood by using the concepts of agents, environments, states, actions and rewards. This is an area of machine learning; where there’s no answer key, but RL agent still has to decide how to act to perform its task. The agent is inspired by behaviourist psychology who decide how and what actions will taken in an environment to maximize some notion of cumulative reward.
System does self-discovery of patterns, regularities and features etc. from the input data and relations for the input data over output data. Discovering similarities and dissimilarities to forms clusters i.e. self-discovery is main target here. Since the examples given to the learner are unlabeled
Machine learning algorithms “learns” from the observations. When exposed to more observations, the algorithm improves its predictive performance. What’s going to happen to the stock market tomorrow? Is a task of deducing function from labeled training data.
Machine learning is subset to Artificial Intelligence which borrows principles from computer science. It is not an AI though; It is focal point where business and experience meet emerging technology and decides to work together.