Data Science Ryan

This is @RyanAllred's #DataScience specific account. I run where I teach Data Science.

[Nick Singh | The Data Science Guy 📕 @NickSinghTech]

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!

[Chris Bakke @ChrisJBakke]

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

Welcome to the future:

[Data Science Ryan @DataScienceRyan]

@rrherr Ooh, interesting. I’ll take a look.

[Data Science Ryan @DataScienceRyan]

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.

[Data Science Ryan @DataScienceRyan]

Every time I look into this I'm still surprised how divided the community is on this topic.

[Data Science Ryan @DataScienceRyan]

@Austen I thought they just had to ask Cursor to build it.

[Data Science Ryan @DataScienceRyan]

Tonight's Livestream:
https://t.co/CpJcbJsOgI

[Data Science Ryan @DataScienceRyan]

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 ->

[Temzee @Temzee_]

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 model yet! Join us at ->

[Temzee @Temzee_]

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

[Temzee @Temzee_]

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

[Temzee @Temzee_]

Tonight's "Intro to Gradient Descent" Machine Learning Workshop starts right now! https://t.co/PVXPShH8bN

[Data Science Ryan @DataScienceRyan]

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 https://t.co/n3WemC4p5t

[Data Science Ryan @DataScienceRyan]

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

See you Thursday!

[Temzee @Temzee_]

Tonight's Machine Learning Workshop - Intro to Linear Regression is starting now!:

https://t.co/wxNqPtVvfn

[Data Science Ryan @DataScienceRyan]

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.

[⁎⁺˳✧༚ 𝚗𝚊𝚗𝚊𝚖𝚒 🎀💛💙🇺🇦💙💛 @hot_girl_spring]

this is how you look when you comment your code

[Data Science Ryan @DataScienceRyan]

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

[Sheel Mohnot @pitdesi]

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.