Can You Learn Machine Learning Without Linear Algebra?

Machine learning is a field that has emerged out of numerous innovations in computational sciences, spanning centuries. So, can a machine learning enthusiast skip linear algebra and flourish? The short answer is — NO. 

However, that’s not a complete picture.

Linear Algebra is a branch of mathematics that is widely used throughout science and engineering. Good understanding of linear algebra is essential for understanding and working with many ML algorithms, especially deep learning algorithms. 

To understand this better, we are listing down the areas where an ML enthusiast will run into  linear algebra in the preliminary stages of machine learning:

Scalars, Vectors, Tensors: Finding the modulus (size), the angle between vectors (dot or inner product) and projections of one vector onto another and to examine how the entries describing a vector will depend on what vectors we use to define the axes 

Matrices:Matrices can transform a description of a vector from one basis (set of axes) to another. For example, figuring out how to apply a reflection to an image and manipulate images.

Length squared sampling in matrices, Singular value decomposition, Low-rank approximation are few techniques which are widely used in the data processing.

For example, the singular value decomposition finds the best-fitting k-dimensional subspace for k= 1,2,3,…, for the set of N data points. Here, “best” means minimising the sum of the squares of the perpendicular distances of the points to the subspace, or equivalently, maximising the sum of squares of the lengths of the projections of the points onto this subspace.

SVD is traditionally used in the principal component analysis (PCA), which in turn is popularly used for feature extraction and for knowing how significant the relationship among the features or properties is to an outcome.

The word ‘mathematics’ brings in a ton of concepts — and this might scare away the beginners.