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[Temzee @Temzee_]

Let's learn data science together. Join our discord community!

https://t.co/D0RoMAucab

[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_]

https://t.co/tSWginV5L6

[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_]

See you then! https://t.co/b3SigbXJ6e

[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

[Temzee @Temzee_]

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_]

So sorry! I was testing some new tools earlier in the week and did a “private” livestream to see how it would look on YT. But the streaming software remembered that setting and made the *next* stream private too. Now that I know that this can happen I’ll make sure it doesn’t in the future. :)

[Temzee @Temzee_]

Last Night's Machine Learning Workshop didn't go exactly as planned. I accidentally set the livestream to "private" so that attendees could only access the recording and couldn't see the livestream in realtime! 🤦‍♂️

So, in case you tried to attend and couldn't, here's the recording of the workshop so that you can still get the information:

https://t.co/Uj11Q47ong

Thanks to all those who have been supporting this ongoing series!

[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.

[Temzee @Temzee_]

Thanks to all those who came out to Tuesday's Machine Learning Workshop. If you missed the livestream you can view it on YouTube here: https://t.co/IIIfT572Bi

Or, browse any of our past livestreams on the Temzee website: https://t.co/ShuHmBO9hD

[Temzee @Temzee_]

Tonight's livestream about bivariate exploratory data analysis (EDA) starts now! https://t.co/hblJN4NE3u

[Data Science Ryan @DataScienceRyan]

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 discussion about the statistics concepts of covariance and correlation!

Join the livestream at 8pm ET on the Temzee YouTube Channel:
https://t.co/9ebXUmHebH

Or, catch the archived recording after the fact at:
https://t.co/E0WKAlTG7x

[Temzee @Temzee_]

The recording from last night's livestream is now up on https://t.co/LBUNLEDA9F:

https://t.co/8wA7frFxS9

You can browse all past livestreams and RSVP for future ones on the Machine Learning Workshop Page:

https://t.co/ShuHmBO9hD

[Temzee @Temzee_]

Tonight's Machine Learning Workshop all about data visualizations for Exploratory Data Analysis starts now! -> https://t.co/I6fF3KM6jC

[Temzee @Temzee_]

At tonight’s Machine Learning workshop (in 30 minutes at 8pm ET), We will be continuing our discussion of univariate analysis from last week with a focus on data visualizations. Livestream happening at -> https://t.co/AzMjt1SV5E

We’ll talk about:
- Data Visualizations for categorical vs continuous features
- Histograms
- Density Plots
- Box Plots
- Identifying outliers and what to do about them
- Bar Charts
- Stacked Bar Charts
- Pie Charts

We should be wrapping up univariate exploratory data analysis today, and next week we’ll be moving deeper into some introductory statistics topics as we move on to Bivariate Exploratory Data Analysis (which will end up being extremely important to our machine learning modeling).

A reminder that I’ve added a new section to https://t.co/DW4074OBp9 where you can browse all past Machine Learning Workshops and get access to any materials (notebooks, slides, etc) that were used during those sessions. You can also RSVP for future Workshops on this same page! ->

https://t.co/XK8Zfpt2YK

[Data Science Ryan @DataScienceRyan]

Daily Code Challenge: https://t.co/MtzMyZorYg

Can you write a function that will generate a matrix (2D List or NumPy array) full of single-digit numbers, of any indicated dimensions?

We'll pass-in the number of rows and columns, your function should do the rest.