Dimensionality reduction is the process of reducing the number of random variables of the program under consideration, by obtaining a set of principal variables. For example, a simple email classification problem, where we need to classify whether the email is spam or not. This can involve a large number of features, such as whether or not the e-mail has a generic title, the content of the e-mail, whether the email uses a template, etc. Here is the diagrammatic representation of dimensionality reduction:

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Dimensionality reduction is a Machine Learning Technique of diminishing the measure of Random Variables in a Problem by acquiring a lot of head Variables. So for more insights about Dimensionality reduction visit this Machine Learning Course.