Linear Regression Fit Point by Point — Topic 75 of Machine Learning Foundations
#MLFoundations #Calculus #MachineLearning
In this video, I introduce the mathematically simplest machine learning model I could think of: a regression line that we fit to data points one by one, single point by single point. This simple model will enable us, in the next video, to derive the simplest-possible partial derivatives for calculating a machine learning gradient. The Machine Learning Foundations pieces really start coming together now — let’s dig right into it!
There are eight subjects covered comprehensively in the ML Foundations series and this video is from the fourth subject, "Calculus II: Partial Derivatives & Integrals". More detail about the series and all of the associated open-source code is available at github.com/jonkrohn/ML-foundations
The playlist for the Calculus subjects is here: • Calculus for Machine Learning
Jon Krohn is Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and leading industry conferences, as well as online via O'Reilly, his YouTube channel, and the SuperDataScience podcast.
More courses and content from Jon can be found at jonkrohn.com.