Just 3 days ago, I had the pleasure of watching the #rstudioconf2022 kick off.
I've been attending since 2018 and watching even longer than that.
And, I was just a normal spectator in the audience until this happened.
@topepos and @juliasilge's keynote showed all of the open source work their team has been working on to build the best machine learning ecosystem in R called #tidymodels.
And then they brought this slide up.
Max and Julia then proceeded to talk about how the community members have been working on expanding the ecosystem.
- Text Recipes for Text
- Censored for Survival Modeling
- Stacks for Ensembles
And then they announced me and my work on Modeltime for Time Series!!!
I had no clue this was going to happen.
Just a spectator in the back.
My friends to both sides went nuts. Hugs, high-fives, and all.
My students in my slack channel went even more nuts.
Throughout the rest of the week, I was on cloud-9.
My students that were at the conf introduced themselves.
Much of our discussions centered around Max & Julia's keynote and the exposure that modeltime got.
And all of this wouldn't be possible without the support of this company. Rstudio / posit.
So, I'm honored to be part of something bigger than just a programming language.
And if you'd like to learn more about what I do, I'll share a few links.
The first is my modeltime package for #timeseries.
This has been a 2-year+ passion project for building the premier time series forecasting system.
It now has multiple extensions including ensembles, resampling, deep learning, and more.
These 7 statistical analysis concepts have helped me as an AI Data Scientist.
Let's go: 🧵
Step 1: Learn These Descriptive Statistics
Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand what’s in front of them in their data sets.
2. Learn Probability
Know your distributions (Normal, Binomial) & Bayes’ Theorem. The backbone of modeling and reasoning under uncertainty. Central Limit Theorem is a must too.
K-means is an essential algorithm for Data Science.
But it's confusing for beginners.
Let me demolish your confusion:
1. K-Means
K-means is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection.
2. Unsupervised:
K-means is an unsupervised algorithm used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset.
🚨 BREAKING: IBM launches a free Python library that converts ANY document to data
Introducing Docling. Here's what you need to know: 🧵
1. What is Docling?
Docling is a Python library that simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
2. Document Conversion Architecture
For each document format, the document converter knows which format-specific backend to employ for parsing the document and which pipeline to use for orchestrating the execution, along with any relevant options.
Type 1 and Type 2 errors are confusing. In 3 minutes, I'll demolish your confusion. Let's dive in. 🧵
1. Type 1 Error (False Positive):
This occurs when the pregnancy test tells Tom, the man, that he is pregnant. Obviously, Tom cannot be pregnant, so this result is a false alarm. In statistical terms, it's detecting an effect (in this case, pregnancy) when it actually doesn't exist.
2. Type 2 Error (False Negative):
This happens when Lisa, who is actually pregnant, takes the test, and it tells her that she's not pregnant. The test failed to detect the real condition of pregnancy. In statistical terms, it's failing to detect a real effect (pregnancy) that is there.