Learn Dimensionality Reduction

Dimensionality reduction is an essential technique in data science and machine learning. The goal of dimensionality reduction is to transform the high-dimensional input data into a lower-dimensional representation while preserving the essential information. This is important for several reasons:

1. Curse of dimensionality: As the number of features increases, the volume of the feature space grows exponentially. This can lead to overfitting, poor generalization, and increased computational complexity. Dimensionality reduction helps mitigate the curse of dimensionality.
2. Visualization and interpretation: Lower-dimensional representations of data are easier to visualize and interpret. This can provide valuable insights into the underlying structure of the data.
3. Improved model performance: Reducing the number of features can lead to more efficient and accurate machine learning models, as the models have fewer parameters to learn and are less prone to overfitting.

With the guided projects and courses below, you will learn the different ways of performing dimensionality reduction on your data.

As a prerequisite, it is recommended to be familiar with matrix operations. A guided project is also provided at the end of this page that teaches you matrix operations for data science and machine learning.