Categorization of Machine Learning Coordinates || Machine Learning Models with Python

Categorization of Machine Learning Coordinates || Machine Learning Models with Python

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⭐ What is Machine Learning? ⭐
In 1959, Arthur Samuel first used the term "machine learning." He established the field of machine learning as "the study that provides computers the power to learn without being explicitly taught," and he was a pioneer in artificial intelligence and computer gaming.

Machine learning, to put it simply, is an application of artificial intelligence (AI) that allows the software to learn from its mistakes and become better at a task without being explicitly programmed. How would you, for instance, create a computer that can recognize fruits according to their numerous characteristics, such as color, shape, size, or any other characteristic?

Supervised Machine learning:
Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labeled. This means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.

Unsupervised Machine learning:
Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training in data. Unlike supervised learning, no teacher is provided which means no training will be given to the machine. Therefore the machine is restricted to finding the hidden structure in unlabeled data by itself. For instance, suppose it is given an image having both dogs and cats which it has never seen.

Reinforcement Machine Learning:
It is neither based on supervised learning nor unsupervised learning. Moreover, here the algorithms learn to react to an environment on their own. It is rapidly growing and moreover producing a variety of learning algorithms. These algorithms are useful in the field of Robotics, Gaming, etc. For a learning agent, there is always a start state and an end state. However, to reach the end state, there might be a different path. In Reinforcement Learning Problem an agent tries to manipulate the environment. The agent travels from one state to another. The agent gets the reward(appreciation) for success but will not receive any reward or appreciation for failure. In this way, the agent learns from the environment.

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