Data Science is the buzzword, and a lot of enthusiasts are interested in learning its fundamentals to make a lucrative career in this field. Linear Algebra is one of the important concepts to learn how to perform data transformation techniques like pre-processing, dimensionality reduction, etc. There are many courses available at your fingertip, but it is difficult to choose the right course suited for your requirement. That's precisely the intent of this post - it makes your course search easy by listing down the five free courses to learn linear algebra foundations for data science. Before I go straight into listing down the courses for you, let me first explain the commonly asked questions – why do we need to learn linear algebra in the first place?

Linear Algebra is usually a prerequisite of machine learning. However, one doesn't need to know all the concepts in linear algebra. In this course, I have compiled together all the important linear algebra concepts that are most frequently used in machine learning. This is the content I taught at Polytechnique Montreal as a refresher on linear algebra for machine learning. Understanding these concepts will help you navigate through an introductory course in machine learning.

Machine learning consists of several algorithms suited for different real-life problems. Anyone with a solid programming foundation can become a good machine learning engineer using ready-made tools, libraries, and models. But if you want to become a real specialist in the field, you cannot escape learning some of the concepts of Linear Algebra. All the magic that happens under the hood of any machine learning algorithm, especially Deep Learning, is mostly Linear Algebra math. So, before I started learning ML, many were dead set that Linear Algebra is a big prerequisite.

Mathematics is the core of all machine learning algorithms. And while it isn't a prerequisite to have formal math education in order to become a data scientist, you need to understand the principles of the subject well enough to successfully build models that add value. In an article I wrote previously, I explained the three branches of mathematics that were essential to gain a deeper understanding of ML algorithms -- statistics, calculus, and linear algebra. This article will solely focus on linear algebra, as it forms the backbone of machine learning model implementation. Linear algebra concepts like vectorization allow for faster computation speeds, and are implemented in libraries like Pandas, Scipy, and Scikit-Learn.

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Graphical representation is also very helpful to understand linear algebra. I tried to bind the concepts with plots (and code to produce it). The type of representation I liked most by doing this series is the fact that you can see any matrix as linear transformation of the space. In several chapters we will extend this idea and see how it can be useful to understand eigendecomposition, Singular Value Decomposition (SVD) or the Principal Components Analysis (PCA). In addition, I noticed that creating and reading examples is really helpful to understand the theory. It is why I built Python notebooks.

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This chapter is mainly on the dot product (vector and/or matrix multiplication). We will also see some of its properties. Then, we will see how to synthesize a system of linear equations using matrix notation. This is a major process for the following chapters.

This chapter is mainly on the dot product (vector and/or matrix multiplication). We will also see some of its properties. Then, we will see how to synthesize a system of linear equations using matrix notation. This is a major process for the following chapters.

Linear algebra is a field of mathematics that could be called the mathematics of data. It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started in machine learning. This is misleading advice, as linear algebra makes more sense to a practitioner once they have a context of the applied machine learning process in which to interpret it. In this post, you will discover why machine learning practitioners should study linear algebra to improve their skills and capabilities as practitioners. Before we go through the reasons that you should learn linear algebra, let's start off by taking a small look at the reason why you should not.