What is Machine Learning?¶
Machine Learning refers to a range of computer algorithms that improve through experience.
What are different types of Machine Learning?
Machine Learning Algorithms can be classified into the following major categories:
Supervised Learning refers to type of machine learning algorithms that uses known outputs for a set of inputs to predict the outputs for other inputs.
1.1.Regression [read more]
Regression is a supervised learning where the output belongs to a continuous interval of numbers.
1.2.Classification [read more]
Classification is a supervised learning where the outputs belong to a discrete set of categories.
Unsupervised Learning refers to type of machine learning algorithm that looks for patterns in data without any previous knowledge on these patterns.
2.1.Clustering [read more]
Clustering is an unsupervised learning where data are grouped into clusters such that members of each cluster are more similar than members of other clusters.
2.2.Associative Rule Learning [read more]
Associative Rule Learning is an unsupervised learning where interesting association between input data are discovered.
2.3.Dimensionality Reduction [read more]
Dimensionality Reduction is a method for mapping high dimensional inputs into a lower dimension often with the goal preserving most information and hence can be categorized as unsupervised learning.
2.4.Anomaly Detection [read more]
Anomaly Detection refers to identification of rare occurances in the data and may be achieved through supervised, unsupervised or semisupervised methods.
3.Semisupervised Learning [read more]
Semisupervised Learning refers to type of machine learning algorithm where limited set of known input-output is utilized (as opposed to Supervised Learning where sufficient amount of input-output is present) to predict the output for other input.
4.Reinforcement Learning [read more]
Reinforcement Learning refers to type of machine learning algorithm where a balance between exploration (of uncharted territory) and exploitation (of current knowledge) is used to maximize the cumulative reward function.