- Getting started with Python
- Variables and arithmetic
- The numpy module
- The matplotlib module
- Vectors and scalar multiplication
- The vector dot product
- Transposing vectors and matrices
- Matrix multiplication
- The matrix inverse
- Bonus material
This course provides an introduction to using Python to learn linear algebra. It is designed for people who have no (or little) previous exposure to Python or to linear algebra.
What is linear algebra?
Linear algebra is the branch of mathematics that deals with vectors and matrices. A vector is a list of numbers, and a matrix is a spreadsheet of numbers.
That sounds really simple, but linear algebra is at the heart of nearly all applied mathematics, including statistics, machine learning, AI, deep learning, image processing, telecommunications, video games, computer graphics, biomedical signal processing, and the list goes on and on...
Why use Python to learn linear algebra?
Many people find math difficult but coding easier. You will be amazed at how much better you can learn math by using Python as a tool.
What will you learn in this course?
You will learn the basics of getting started with using Python and with using Python to learn mathematics. You'll see an overview of the major topics in linear algebra, although I do not go into a lot of depth on any particular topic.
By the end of this course, you will know enough to decide whether you want to learn more about Python and math.
What do you need to know before enrolling?
Well, you need to know how to use a computer. But you don't need to know anything about computer programming or linear algebra. The only thing you really *need* for this course is the willingness to dedicate 2-3 hours of your time to learning something new.
What do you have to lose?
The entire course takes 2-3 hours to complete (2 hours of video content, and about an hour to complete the practice problems). This is a great way to see whether you want to continue studying Python for math and linear algebra. And if you decide that this isn't right for you, then you only spent a few hours on it, rather than investing in tens of hours and money. Really, you have nothing to lose!
Who is your instructor?
I have been teaching data analysis, scientific programming, statistics, and signal processing for almost 20 years. I have several best-seller courses here on Udemy and my courses have well over 10,000 high-ranked reviews (don't believe me -- check out the reviews on this and my other courses!). I take online teaching seriously (although I let a few jokes slip through now and then...), and I remain actively involved in making sure my courses are high quality and up-to-date.
Filtering, SUMIFS, Pivot Tables, Conditional Formatting
I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations.
But you're here because of my teaching, so let me tell you about that:
I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way.
I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style.
Over 94,000 students have watched over 6,500,000 minutes of my courses (that's over 12 years of continuous learning). Come find out why!
I have several free courses that you can enroll in. Try them out! You got nothing to lose ;)
By popular request, here are suggested course progressions for various educational goals:
MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing
Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python
Applied linear algebra: Complete Linear Algebra; Dimension Reduction
Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing