FreeCodeCamp released today a new course about Machine Learning Safety by Dan Hendrycks from the Center for AI Safety. The course focuses on the safety element in ML research. This 8 and half hours course covers topics such as:
✅ Research topics in ML safety
✅ Define different potential hazards and risks
✅ Methods for identifying and reducing risks
✅ Dive into different ML topics such as vision, NLP, and RL
✨ What is unlist()?
The unlist() function in R is like a magician that takes complex nested lists or vectors and transforms them into a simple atomic vector. It's a game-changer when dealing with intricate data structures, allowing you to flatten them with ease.
The minimum effect is my power threshold so they cancel each other out. How can I do this? Preferably with linear models in r (I like emmeans and simr.
@lakens you wrote that more power is needed for minimum effects compared to null tests, so you might know.
5 Latest Tools You Should Be Using With Python for Data Science.
🗂️ The article provides insightful details on tools like ConnectorX, DuckDB, Optimus, Polars, and Snakemake which could enhance data wrangling, querying, manipulation, and workflow automation capabilities.
An early version of the Modern Data Science with R 3rd edition is now available online. The book by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. focuses on core data science topics such as:
✅ Data visualization
✅ Data wrangling
✅ Statistics and ML models
✅ General data science topics
if you're ready to level up your data manipulation skills, give intersect() a spin and let your insights shine! 🌈 Embrace the world of R and keep growing as a data wizard! 🧙♂️ Happy coding! 🎉
Do you have any Substacks to recommend? Do you write a Substack I might be interested in? I’d love to follow! I feel like I’m very much underutilizing Substack.
🐞 Debugging & Cleaning Broken Code @andrew
📈 Jazz up your ggplots! @USGS_DataSci
🌈 HTML & CSS for R Programmers @rappa753
We have many ways you can help the R-Weekly Project. Check the episode show notes for details, and have some fun by grabbing a new podcast app like @podverse to send us a fun boost! https://newpodcastapps.com
I somehow missed that Red Hat abruptly ended Operate First - the #datascience stuff in OpenShift. I had been evaluating it earlier this year in my day job and it looks like I may have definitely dodged a bullet by sticking with upstream Jupyter.
There is no better way to learn a topic than using a real-life example. The Introduction to NFL Analytics with R is a new book by Bradley J.Congelio, focusing on NFL analytics using R, as the name implies. The book covers the following topics:
✅ Introduction to NFL analytics with R
✅ Working with NFL data
✅ Data visualisation applications ❤️
✅ Analysis and modeling of NFL data
Wer macht Dinge mit #DataScience, ist auf dem #CCCamp23 und hätte Lust auf ein kurzes Interview?
Wir planen eine datenleben-Folge mit Kurzinterviews von Data Science Menschen. Was macht ihr? Wie seid ihr da hingekommen? Was begeistert euch an Data Science? So in die Richtung. @naerrin wird mit Mikro und den Fragen auf dem Camp zugegen sein und würde gerne Stimmen einfangen, die dann später zu einer schönen Folge zusammengeschnürt werden sollen. Meldet euch gern via Direktnachricht.
If you're interested in the topic of "Biases and inequalities in machine learning for healthcare", please apply to come and work with me and Prof Jo Knight as part of the CHICAS research group at Lancaster Medical School!
Mona-openai is a new Python package by mona that enables capturing logs to monitor your OpenAI API usage 🚀. That includes cool features such as:
✅ Hallucination alerts
✅ Tokens usage
✅ Behavioral drifts and anomalies
✅ LangChain support
This is a great article by Michael Levinger about the applications of explainable AI for identifying fraud and preventing it. Explainable AI methods help to make black-box models more interpretable and visible. That includes methods such as:
✅ Feature Importance
✅ LIME and SHAP methods
✅ Rule-based Models
✅ Data Visualization
Yesterday a good friend of mine helped me to understand and toy around with Gnu #Guix in a VM as I'm very hesitant to add anything to my daily driver machine. All things considered, I'm >90% convinced.
But here is a question for the friends and the community: What are the advantages of Nix over Guix (apart from number of packages)?
P.s: I'm going to have it on an Arch-based machine to add reproducibility to my projects. It will not handle my OS packages.
Looking for a recommendation(website,Substack, any other material...) where I can improve my SQL knowledge. I am looking for something that I can read(theory) and practice(exercisea). I really enjoy learning python in Substack but until now I have not found something similar for SQL.