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- Introduction
- What is Linear Algebra?
- Why Linear Algebra?
- Important Linear Algebra Concepts for Machine Learning
- Conclusion

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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