Artificial Intelligence and Machine Learning

Reinforcement learning is an area of Machine Learning. Reinforcement. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, it is bound to learn from its experience. Supervised vs Reinforcement Learning: In supervised learning, there’s an external “supervisor”, which has knowledge of the environment and who shares it with the agent to complete the task. But there are some problems in which there are so many combinations of subtasks that the agent can perform to achieve the ob...

Supervised learning as the name indicates a presence of supervisor as teacher. Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with correct answer. After that, machine is provided with new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces an correct outcome from labeled data. For instance, suppose you are given an basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:     If shape of object is rounded and depression at top having color Red then it will be labelled as –Apple.     If shape of object is long curving cylinder having color Green-Yellow then it will be labelled as –Banana. Now suppose after training the data, you have given a new separate fruit say Banana from basket and asked to ident...