Eigenvectors and Eigenvalues — Topic 28 of Machine Learning Foundations

In this video, I leverage colorful illustrations and hands-on code demos in Python to make it intuitive and easy to understand eigenvectors and eigenvalues, concepts that may otherwise be tricky to grasp. There are eight subjects covered comprehensively in the ML Foundations series and this video is from the second subject, "Linear Algebra II: Matrix Operations". More detail about the series and all of the associated open-source code is available at github.com/jonkrohn/ML-foundations The next video in the series is: youtu.be/GHZcmWHZhJY The playlist for the entire series is here: youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a This course is a distillation of my decade-long experience working as a machine learning and deep learning scientist, including lecturing at New York University and Columbia University, and offering my deep learning curriculum at the New York City Data Science Academy. Information about my other courses and content is at jonkrohn.com Dr. Jon Krohn is Chief Data Scientist at untapt, and the #1 Bestselling author of Deep Learning Illustrated, an interactive introduction to artificial neural networks. To keep up with the latest from Jon, sign up for his newsletter at jonkrohn.com, follow him on Twitter @JonKrohnLearns, and on LinkedIn at linkedin.com/in/jonkrohn