Introduction To Machine Learning
What Is Machine Learning
The term Machine Learning was coined by “Arthur Samuel” in 1959 an American pioneer in the field of computer gaming and Artificial Intelligence and stated that, “Machine Learning is the field of study that gives computer the ability to learn without being explicitly programmed.” Machine Learning is one of the most existing technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans, “The ability to learn” .
Types Of Machine Learning Algorithm
There are various types to classify machine learning but we discuss some of few Important.
1. Supervised Machine Learning -
When an algorithm learn from example data and associated target response that can consist of numeric values or string labels such as classes or tags in order to later predict the correct response when posed with new example comes under the category of Supervised Learning.
2. Un-Supervised Learning-
When an algorithm learns from plain example without any associated response, leaving to the algorithm to determine the data patterns on its own. This type of algorithm tends to restructure the data into something else such as new features that may represent a class or a new series of un-correlated values. They are into the meaning of data and new useful inputs to Supervised Machine learning algorithm.
3. Semi-Supervised Learning-
Where an Incomplete training signal is given : A training set with some of the target outputs missing. There is a special case of this principle known as Transduction where the entire set of problem instances is known at learning time, except the part of the targets are missing.
4. Reinforcement Learning-
When you present the algorithm with examples that lack labels as in Un-supervised learning. However, you can accompany an example with positive or negative feedback according to the solution the algorithm proposes comes under the category of Reinforcement Learning, which is connected to applications for which the algorithm must make decision and the decision bear consequences.
