### What is the difference between Feature Engineering and Feature Selection?

Feature Engineering is the process of using domain knowledge to extract numerical representations from raw data.

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Categorical features are features that can take a limited, and usually a fixed number of possible values.

In the context of Machine Learning, ‘Sparsity’ is used to explain the degree of ‘emptiness’ of the data within a data structure.

Discretization refers to the process of binning a continuous variable into a discrete number of buckets.

This refers to a special case of discretization in which a continuous variable is transformed into a categorical representation that only consists of two bins.

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- Machine Learning 101 (30)
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- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
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