Linear Algebra for Machine Learning

Anmol Sharma

8 months ago

Linear Algebra for Machine Learning | insideAIML
Table of Content
  • Introduction
  • What is Linear Algebra?
  • Why Linear Algebra?
  • Important Linear Algebra Concepts for Machine Learning
  • Conclusion

Introduction

          Linear Algebra to Machine learning as petrol to the car. It is the foundation of ML from notations to describe algorithms to implementing algorithms. It is necessary to learn Linear Algebra if you want to master Machine learning. It will help you in understanding and developing a better intuition for Machine learning algorithms. So, let’s understand the importance of Linear Algebra for Machine learning.

What is Linear Algebra?

          Linear Algebra is another tool of mathematics that deals with linear functions and linear equations. These fu-nctions and equations are represented through vectors and matrices. It extends algebra to an arbitrary number of dimensions.

Why Linear Algebra?

          Now, you might be wondering why we need to learn Linear Algebra for Machine learning. Below are some of the reasons to answer this question.
Understanding Dataset 
          The dataset we use for training our machine learning model is in the form of a matrix. When we spilt our table for training purposes the labels are in vector form. Both matrix and vector are part of Linear Algebra. So, to understand your dataset, you definitely need an understanding of Linear Algebra.
Understanding Algorithms
          Most Machine learning algorithms use linear equations and notations. Having the knowledge of Linear Algebra will make it easy for us to understand these algorithms and we can even implement them from scratch. Also, we will have a strong intuition of these algorithms for how they work.
Understanding ML Techniques
          Many Machine learning techniques use vectors and matrices for computation. If we have a good understanding of Linear Algebra we can easily learn how these ml techniques work and when to apply which technique.
Computer Vision
          Images are used for computer vision and these images are stored in the form of a matrix. To excel in computer vision you definitely need to understand Linear Algebra.
Statistics
          Like Linear Algebra, Statistics also play an important role in Machine learning. Having an understanding of    Linear Algebra is a must for understanding advanced Statistics concepts. 

Important Linear Algebra Concepts for Machine Learning

          Linear Algebra is a very vast field. Below I have listed the Linear Algebra concepts that are important for    Machine learning.
  • Sets
  • Relations and Functions
  • Vectors
  • Matrices
  • Linear and Affine mappings
  • Determinant

Conclusion

          Linear Algebra is a building block for Machine learning. In this article, we learned what is Linear algebra, why it is important for Machine learning and the important Linear Algebra concepts for Machine learning. We discussed the reasons why should one learn Linear Algebra before starting Machine learning. We encourage you to learn Linear Algebra before starting your Machine learning journey.
We hope you gain an understanding of what you were looking for. Do reach out to us for queries on our, AI    dedicated discussion forum and get your query resolved within 30 minutes.
   
Liked what you read?  Then don’t break the spree.  Visit our insideAIML blog page to read more awesome articles. 
Or if you are into videos, then we have an amazing Youtube channel as well. Visit our InsideAIML Youtube Page to learn all about Artificial Intelligence, Deep Learning, Data Science and Machine Learning. 
Keep Learning. Keep Growing.

Submit Review