Hi Everyone, today we will learn about one of my favorite Machine Learning algorithms - Linear Regression! We will use it to predict the y location of a given x, as well as the value of a not yet listed house in Vancouver. To fully understand the algorithm, we will take simple inputs, and we will pass them through the formula step by step.
We will see detailed examples of 3 use cases:
⭐ predicting (x,y) coordinates on a graph.
⭐ predicting on a set of entries in a table.
⭐ updating line of best fitting.
By the end of this video, you'll be perfectly comfortable with calculating linear regression - completely on your own! You'll be able to compute the slope, y-intercept, and residuals both with math and with code. So what are we waiting for?? let's figure out Linear Regression once and for all! 💪💪💪
Time Stamps:
00:00 - linear regression overview
00:43 - slope
01:49 - y Intercept
02:37 - find y location with math
03:02 - find y location with code
03:34 - features and targets
04:07 - multi-feature linear regression
06:17 - multi-feature code
06:35 - weights
07:18 - sci-kit learn code
07:49 - 3 point linear regression math
09:34 - 3 point linear regression code
Credits:
💳 Thumbnail and presentation graphics by Freepik.
💳 Icons by Flaticon.