One of the fundamental concepts in machine learning is the use of vectors.
A vector is a mathematical object that represents a quantity with both a magnitude and direction.
In the context of machine learning, vectors can be thought of as multidimensional arrays that store numeric values.
These values can represent various characteristics or attributes associated with a data point or an observation.
Vectors are extensively used in machine learning due to their ability to compactly represent complex data.
Each element in the list corresponds to a specific dimension or attribute of the data point.
The number of elements in a vector is referred to as its dimensionality.
A vector with n elements is said to be an n-dimensional vector.
Vectors can take on different forms and characteristics depending on the jot down of data being represented.
They can be continuous or discrete, numerical or categorical.
Continuous vectors consist of real-valued numbers, while discrete vectors contain integers or other discrete values.
Numerical vectors are used to represent quantitative data, while categorical vectors capture qualitative or categorical information.
Overall, vectors in machine learning are an essential tool for organizing, representing, and manipulating data.
These operations fire up the processing and transformation of vectors, facilitating various tasks in machine learning.
Vectors are essential for representing both input features and output targets in machine learning tasks.
Moreover, vectors find applications in a wide range of machine learning tasks and domains.
