RAISE YOUR RIGHT HAND AND REPEAT AFTER ME: We remove outliers when their extremeness indicates they're not part of our population โ we do not get rid of them just because they are extreme!
What is your go-to virtual environment tool in Python? Twitter only lets me add four options to the poll, so if your tool of choice isn't listed, reply with your fav.
Tonight's Machine Learning Workshop is about to kick off.
We'll be going over regularization techniques for linear regression (Ridge, Lasso, ElasticNet etc.) in addition to a whole host of other topics.
Link Below ->
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 โresulting in our most powerful model yet! Join us at ->
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
Here's the recording of last week (Thursday's) Machine Learning Workshop where we learned about the basics of Gradient Descent.
https://t.co/B2wlnN27lo
We used Gradient Descent fit a simple linear regression model, and due to the model only having two parameters that enabled us to create some cool graphs and visualizations to better understand exactly how gradient descent discovers the model's best parameters.
This week we'll be back to our regularly scheduled Tuesday at 8pm Eastern Machine Learning Workshop. Tomorrow we'll be fitting the absolute best predictive Linear Regression model that we can in the time that we have together. https://t.co/AzMjt1Sng6
Tonight's Machine Learning Workshop Livestream will feature an introduction to Gradient Descent โperhaps the most important algorithm in all of machine learning! Join us, 8pm Eastern, at https://t.co/n3WemC4p5t
Tonight's Machine Learning Workshop (8pm Eastern - on https://t.co/9ebXUmHebH) 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 Squared Error (MAE), and Root Mean Squared Error (RSME).
We'll then fit the best model possible and discuss how exactly the algorithm decides what the best slope and intercept parameters are. We'll introduce the concept of loss functions and we'll visualize our model's loss function in 3D!
We'll finish things off by generating some predictions and making our first submission to Kaggle using this introductory model.
Taco Bell is leading the way in innovation yet again. They are rolling out AI ordering at drive-thrus. In tests, the AI is outperforming human accuracy, decreasing wait times and improving employee happiness.