
Get started with Python by running online notebooks via Jupiter notebook and Binder, learn to save, download, and upload ipynb files, and know when to install Python locally with Anaconda.
Master variables, arithmetic operations, and printing in Python for absolute beginners, including assignment, concatenation, type conversion, and basic debugging.
Learn to create and use lists in Python, import the numpy module as np, and access functions like mean, sqrt, and linspace for linear algebra tasks.
Learn the matplotlib module for visualizing data in Python, plotting lines and images, with legends and labels. Practice creating line plots, ranges, and a red x across a matrix.
Learn the vector dot product: compute a scalar by pairing elements and summing, check dimensionality, and see orthogonality as zero dot product with two-dimensional vectors and plotting.
Explore matrices as two-dimensional data structures with rows and columns, learn square and diagonal matrices, examine symmetric, identity, and diagonal matrices, and practice scalar multiplication and addition in Python.
Explore the transpose operation on vectors and matrices in Python, converting columns to rows and revealing that double transposing returns the original, while applying to non-square matrices remains valid.
Determine when matrix multiplication is valid by matching inner dimensions. Learn left versus right multiplication, and see how transpose, identity, and zeros matrices affect product symmetry.
Explore why matrix division doesn't exist and how the matrix inverse isolates X by left-multiplying A, yielding the identity; apply to linear least squares and Python numpy tools.
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.