Is It Possible To Learn Machine Learning Sans Linear Algebra?

Just like how a solid foundation is essential for a building, the concepts of linear algebra help understand machine learning theories and provide better intuition which will help make the right decisions.  Linear algebra is undeniably a pillar in the field of machine learning and it is a field of mathematics which can be referred to as the mathematics of data. Areas of mathematics, such as statistics and calculus, will help gain a profound understanding of machine learning. These also require prior knowledge of linear algebra. Though some may argue that linear algebra would be more useful to a practitioner who has a better understanding of machine learning processes, one cannot escape mastering some important theories of linear algebra.


So, now you know the answer to the question, ‘Is it possible to learn machine learning sans linear algebra?’ No.


What is The Linear Algebra?


Linear algebra refers to a continuous form of mathematics which is applied in science and engineering. It allows one to model natural phenomena and compute them efficiently. It is a branch which helps us understand geometric terms like planes, in higher dimensions, and aid in performing mathematical operations on them. It comprises of linear functions and linear equations represented through vectors and matrices.


What is Machine Learning?


It is the process of learning which provides systems with the ability to learn and improve automatically from experience without being programmed explicitly. It focuses on developing computer programs which can access the data and use it to learn on their own. It is a study of statistical models which computer systems use to perform tasks efficiently by relying on patterns and inference instead of using explicit instructions.


How Linear Algebra Helps Machine Learning

1. Linear Algebra Is The Elementary Unit For Machine Learning


Differential calculus and integral calculus precede linear algebra when it comes to learning advanced mathematics. Apart from helping with limits, differentiation, and integration techniques, they help set a base for applying them to vectors and multi-dimensional configurations. It will help in learning concepts such as Vectors in space and the Simplex method, which will be useful for machine learning.

2. The Machine Learning Intuition


It boosts your intuition towards machine learning by offering a wide range of perspectives. It helps develop broad and idealistic thinking. It will help put matrix operations to use, visualize, and develop different plotting graphs, and apply unique and better code.

3. Linear Algebra Helps Improve Your Take On Statistics


Statistics is a vital part of machine learning as it helps organize and assimilate data. Linear algebra acts as a base to develop a solid understanding of statistical concepts. Notations, methods, and operations of linear algebra help assimilate advanced statistical topics such as multivariate analysis. Learning notations, methods, and operations will also help you to:


  • Interpret and implement descriptions of new methods.
  • Describe your methods concisely to other practitioners.
  • Read descriptions of existing algorithms.




As machine learning itself has an array of concepts to learn and master, linear algebra may, at first, be overwhelming to a beginner. Linear equations and linear functions may look puzzling to a majority of the learners. With regular practice and effort, linear algebra will no longer seem like a daunting task. Learning linear algebra provides benefits which are manifold such as improves math and programming skills, and enhanced decision-making skills, and it also prepares a person to think and helps explore the deeper concepts of machine learning.