Programming is a great way to get insights about math concepts. You’ll see here tips and tricks to learn math, more specifically linear algebra, from a coding perspective. You’ll see the relationship between Numpy functions and linear algebra abstract concepts.

At the end of this mini-tutorial, you’ll know what are arrays ,vectors & matrices, and why they are the core of machine learning and data science.. so lets gets started

✅Array are Homogeneous Data Structure
✅Multiple equal length vector stacked as table
✅Wide range of collection operations
✅Data tranformation and its properties.
✅Lots of hands stuff and more...

Use the package manager pip to install below

pip install numpy

• Arrays are the most important family of higher-order collections in machine learning. They are used to represent images, text documents and many other types of data.

• A vector is a tuple of one or more values called scalars. Vectors are built from components, which are ordinary numbers. You can think of a vector as a list of numbers, and vector algebra as operations performed on the numbers in the list.

• Matrices are rectangular arrays consisting of numbers and can be seen as 2nd-order tensors. If m and n are positive integers, that is m, n ∈ ℕ then the m×n matrix contains mn numbers of elements, with m number of rows and n number of columns.*

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@misc{Charged Neuron,
    author       = {Roja Achary},
    title        = {Vectors, Arrays, Matrices},
    Credits      = {websites,CA,me}
    month        = {November},
    year         = {2021}
}