Uploaded May 2021 | Updated September 2026, 1 week ago

---

#LinearAlgebta #DataScience
In this video tutorial I use Python to explain the easy steps of the Gram Schmidt process. Following the steps of this process yields a set of orthonormal basis vectors for the (sub) space spanned by the column vectors of a matrix. Each new column vector is orthogonal to all the other column vectors and each is of unit length.
This change of basis is used in QR factorization or QR decomposition of a matrix.

WATCH NEXT:
▶️ Modern Linear Algebra using Python: youtu.be/MBhPaqKRehg

▶️ Second order ODEs solved with Python using the Euler formula: youtu.be/pV0SY0EtKaI
▶️ Gram Schmidt process for QR decomposition using Python: youtu.be/fdshjUzUWTs
▶️ Second order ODE solved with RK4 in Python: youtu.be/-k64Xa0onfQ
▶️ Solving a system of first order ODEs with RK4 using Python: youtu.be/cQSQCv0XBx8


//You Might Also Like:
~~~~~~~~~~~~~~~

Udemy: udemy.com/mathematica-for-statistics
Udemy: udemy.com/biostatistics-fundamentals-using-python
Udemy: udemy.com/mathematica
Udemy: udemy.com/spss-for-healthcare-and-life-science-statistics
RPubs: rpubs.com/juanhklopper
Github: github.com/juanklopper?tab=repositories

//CONNECT WITH ME
~~~~~~~~~~~~~~~
👉 Web: juanklopper.com