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:

1.Supervised Learning

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.

2.Unsupervised Learning

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 occurrences 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.