Remember when everybody thought that self-driving was going to wipe out all professional driving jobs in a matter of months? I mean, it's going to happen, just not overnight. Please don't make this same mistake when you see a humanoid robot saw board.

this is how you look when you comment your code

In the context of Linear Regression Modeling, few EDA activities could be more valuable than looking at how our x variables (features) correlate with our y variable (target). That’s what we’ll be building up to tonight as we have a *really* important intuition-building

Smart to start by going after the lowest bar for human performance in the entire industry.

Tonight's Machine Learning Workshop (8pm Eastern - on youtube.com/@temzee) will feature an introduction to Simple Linear Regression. We'll start with a baseline model and introduce the concept of residuals as well as the error metrics of Mean Absolute Error (MAE), Mean

Tonight's Machine Learning Workshop - Intro to Linear Regression is starting now!: youtube.com/live/MfBKyOZ9H…

This week's Machine Learning Workshop will be pushed back from its usual time (tonight) to Thursday at 8pm Eastern. See you Thursday!

Tonight's Machine Learning Workshop Livestream will feature an introduction to Gradient Descent –perhaps the most important algorithm in all of machine learning! Join us, 8pm Eastern, at youtube.com/@temzee

Tonight's "Intro to Gradient Descent" Machine Learning Workshop starts right now! youtube.com/live/E9bbeuww8…

Here's the recording of last week (Thursday's) Machine Learning Workshop where we learned about the basics of Gradient Descent. temzee.com/machine-learni… We used Gradient Descent fit a simple linear regression model, and due to the model only having two parameters that enabled us

At tonight's Machine Learning Workshop (8pm Eastern) we'll be going over introductory basic best practices for Predictive Modeling with Linear Regression. Topics like: - Cross Validation - Filling NaNs - One Hot encoding - Feature Scaling - Pipelines

At Tonight's ML Workshop (8pm eastern) we'll be wrapping up our discussion of predictive modeling with Linear Regression by talking about Regularization techniques (Ridge, Lasso, ElasticNet) in addition to log transforming skewed x and y variables –resulting in our most powerful

Just invested $65M into a new company that uses AI to slice bread. Welcome to the future:

RAISE YOUR RIGHT HAND AND REPEAT AFTER ME: We remove outliers when their extremeness indicates they're not part of our population — we do not get rid of them just because they are extreme!