Let’s dive into the Core Concepts behind Linear Algebra with Examples & Illustrations. This is a series of lessons on Linear Algebra, covering the main ideas behind this important topics. Let’s get started.
Introduction
The world of data science is vast, complex, and intriguing. At the heart of many algorithms, especially in machine learning and deep learning, lies a foundational mathematical tool Linear Algebra.
If you’re diving into data science or just curious about its mathematical backbone, this post is for you.
You will understand the essential components of linear algebra, its significance in Data Science, and learn from tangible examples for better comprehension.
Let’s get started.
1. What is Linear Algebra?
Linear Algebra is a branch of mathematics that deals with vectors, vector spaces, linear transformations, and matrices. These entities can be used to depict and solve systems of linear equations, among other tasks.
Let’s now understand the fundamental concepts used in Linear Algebra.
2. Fundamental Concepts
To get started with linear algebra, you need to understand few basic terms. Let’s define them.
 Vector: A vector is a onedimensional array of numbers. For instance, in a 2D space, a vector v can be represented as
v = [2, 3]
, pointing 2 units in the xdirection and 3 units in the ydirection. 
Matrix: A matrix is a
m x n
, twodimensional array of numbers. It’s essentially a collection of vectors. You can think of numbers arranged in rows and columns. For example:
A = [2 3]
[4 5]
[6 7]
Here, the matrix A
has three rows and two columns.
 Vector Spaces: A vector space is a set of vectors that adhere to specific rules when undergoing addition and scalar multiplication. For a set to qualify as a vector space, it must satisfy properties like commutativity, associativity, and distributivity.

Tensor: An ndimensional array where n can be more than 2.

Linear Transformations: Transformations between vector spaces while preserving the operations of vector addition and scalar multiplication. Matrices can represent these transformations.

Dot Product: This is the sum of the products of corresponding elements of two vectors.
For vectors
a = [a1, a2]
andb = [b1, b2]
, the dot product isa1*b1 + a2*b2
.Example: For vectors
v1 = [2,3]
andv2 = [4,5]
, the dot product is2*4 + 3*5 = 8 + 15 = 23
. 
Matrix Multiplication: Involves taking the dot product of rows of the first matrix with columns of the second matrix.
Example: To multiply matrices A and B, the entry in the 1st row, 1st column of the resulting matrix is the dot product of the 1st row of A and the 1st column of B.

Matrix Addition and Subtraction:
Addition: The element at row i, column j in the resulting matrix is the sum of the elements at row i, column j in the two matrices being added.
Subtraction: Same as addition but with subtraction of elements.

Determinant and Inverse:
Determinant: A scalar value that indicates the “volume scaling factor” of a linear transformation.
Inverse: If matrix A’s inverse is B, then the multiplication of A and B yields the identity matrix.
3. Why is Linear Algebra Essential for Data Scientists?
There are multiple reasons as to why Linear Algebra matters for Data Scientists.
 Foundational to Machine Learning:
Most machine learning algorithms, especially those in deep learning, rely heavily on linear algebra. The idea of matrices and tensors is present everywhere in the world of AI, since Data is stored and worked on such objects.Moreover, concepts like transformations, eigenvalues, and eigenvectors are used in algorithms like Principal Component Analysis (PCA), tSNE which are used for
dimensionality reduction of data and visualizing high dimensional data. 
To represent data:
In data science, data is often represented as matrices or tensors (multidimensional arrays). For example, an image in a computer can be represented as a matrix of pixel values. Understanding how to manipulate these matrices is needed for many data tasks. 
Memory Efficient computations:
Operations on matrices and vectors can be highly optimized in modern computational libraries. Knowing how to use linear algebra allows one to tap into these optimizations, making computations faster and more memory efficient.Libraries like NumPy (in Python) or MATLAB are grounded in linear algebra. These tools are staples in the data science toolbox, and they are designed to handle
matrix operations efficiently. 
Conceptual Understanding:
Beyond the computational benefits, a solid grasp of linear algebra provides a deeper conceptual understanding of many data science techniques. For example, understanding the geometric interpretation of vectors and matrices can provide intuition about why certain algorithms work and how they can be improved. 
Optimization:
Optimization problems in machine learning and statistics, like linear regression, can be formulated and solved using linear algebraic techniques. Techniques such as gradient descent involve vector and matrix calculations. Techniques such as ridge and lasso regression employ linear algebra for regularization to prevent overfitting. 
Signal Processing:
For those working with time series data or images, Fourier transforms and convolution operations, which are rooted in linear algebra, are crucial. 
Network Analysis:
If you’re working with graph data or network data, the adjacency matrix and the Laplacian matrix are foundational, and understanding their properties requires knowledge of linear algebra.
4. Use of Linear Algebra in Machine Learning Algorithms

Neural Networks: Neural networks are made of data connections called Neurons. Each neuron’s output is nothing but a linear transformation (via weights, which are matrices) of the input, passed through an activation function. This is mostly multiplication operations of linear algebra.

Support Vector Machines: SVM’s use the dot product to determine the margin between classes in classification problems. So, use Linear algebra is used.

Image Processing: Filters applied to images are
matrices
that transform the pixels. 
Principal Component Analysis (PCA): PCA is a shining application of linear algebra. At its core, PCA is about finding the “principal components” (or directions) in which the data varies the most.
StepbyStep Process (each involve linear algebra):
Standardization: Ensure all features have a mean of 0 and standard deviation of 1.
Covariance Matrix Computation: A matrix capturing the variance between features.
Eigendecomposition: Find the eigenvectors (the principal components) and eigenvalues of the covariance matrix.
Projection: Data is projected onto the top eigenvectors, reducing its dimensions while preserving as much variance as possible.
Linear algebra concepts is used in Data Science, day in and day out. It offers a framework to manipulate, transform, and interpret data, making it essential for various ML algorithms and processes. As you advance in data science, knowing the foundation in linear algebra will help.