Linear Algebra is an important subfield of mathematics and forms a core foundation of machine learning algorithms. The post shares five free courses to master the concepts of linear algebra.
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.
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There are many courses available at your fingertip, but it is difficult to choose the right course suited for your requirement. Multiple factors play a role to decide the right course such as:
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? How does it relate to data science and machine learning concepts?
Machine learning algorithms typically require knowledge of scalar, vectors, and matrices to compute loss functions, eigenvalues, covariance matrix, etc. Further, linear algebra is also used extensively in neural networks, regularization techniques, recommender systems, Singular Value Decomposition (SVD), Principal Component Analysis (PCA), etc.
Now that we understand the significance of comprehending linear algebra concepts, let’s find out the top five courses to master its concepts for free:
It is a playlist of 16 videos that include concepts such as cross products, dot products, eigenvectors, eigenvalues, etc. 3Blue1Brown is a youtube channel that focuses on teaching mathematical concepts in an easy-to-understand manner using unique visualization techniques. Though it does not qualify as a course per se, the channel has made its way to our list of recommendations because of its unique theme of inventing and visualizing math.
It is a good course to learn the fundamentals of linear algebra like the vector product, linear transformation, finding determinants, etc. If you are looking to quickly revisit the basics, then refer to this link.
It is a short course with a 10-video playlist that focuses on why you need to learn linear algebra for machine learning. The course is a primer to understand linear algebra concepts well within 90 minutes.
Khan Academy and 3Blue1Brown videos are easy to understand and help you pick up the pace as a complete beginner. Once you have learned the concepts from these resources, you are set to learn the deeper and more comprehensive material from the courses suggested below.
The course is taught by Professor Robert van de Geijn and includes short videos and visualizations followed by exercises and programming assignments.
The course spans over 15 weeks and demands a commitment of 6-10 hours per week. The best part is that it is self-coursed and can be completed at the convenience of the learner.
It is available in two formats – verified (paid) and audit (free) track. If you wish to receive a completion certificate and have access to graded assignments and exams, then you need to opt for verified i.e., paid track. You can check more details for each track here.
This course is part of the series – “Mathematics for Machine Learning Specialization” and is a highly rated (4.7/5) course. The USP of this course is that it does not just explain theoretical concepts but also helps the learners to understand and implement these ideas through python code.
Learners who do not have sufficient python background can also get started with this course, as it guides them through short code blocks with focused concepts. It is spread over a period of four weeks and requires 19 hours to complete.
The post has listed five popular courses to master linear algebra. The best part is that all the listed courses are free of cost and ranked from beginner-level to more advanced concepts. While the ML community keeps questioning if it is essential to learn linear algebra to get started with machine learning, I would highly recommend following a more agile approach to keep iterating and referring to these courses as you chart out your ML algorithmic journey.
Vidhi Chugh is an award-winning AI/ML innovation leader and an AI Ethicist. She works at the intersection of data science, product, and research to deliver business value and insights. She is an advocate for data-centric science and a leading expert in data governance with a vision to build trustworthy AI solutions.
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