While data scientists are often taught about training a machine learning model, building a reliable MLOps strategy to deploy and maintain that model can be daunting.
It doesn’t have to be this way!
Join us with Julia Silge at Posit on Wednesday, April 24th at 11 am ET to learn how Posit Team provides fluent tooling for the whole ML lifecycle.
No registration is required to attend - simply add it to your calendar using this link, https://pos.it/team-demo
Meta released today Llama 3, the next generation of the Llama model. LLama 3 is a state-of-the-art open-source large language model. Here are some of the key features of the model: 🧵👇🏼
A major release to Ollama - version 0.1.32 is out. The new version includes:
✅ Improvement of the GPU utilization and memory management to increase performance and reduce error rate
✅ Increase performance on Mac by scheduling large models between GPU and CPU
✅ Introduce native AI support in Supabase edge functions
I had previously discussed how to drop those pesky NA records from your data.frame but now, what if you actually want to inspect them? That is what I cover in today's post.
The Learn R Through Examples by Xijin Ge, Jianli Qi, and Rong Fan provides an introduction to data analysis with R. The book covers the core topics of data analysis using different datasets, from simple and clean datasets to messy and big datasets. 🧵👇🏼
The course Quick & Easy Statistics by Fatai Akemokwe and Afelemo Orilade is on sale on Leanpub! Its suggested price is $44.99; get it for $19.00 with this coupon: https://leanpub.com/sh/z2aczaAD#DataScience
I don't suppose there are any freelance #DataAnalysts interested in mentoring an apprentice middle-aged queer guy with a spotty work history, #MentalIllness and an absolute loathing of and revulsion to traditional performative job interviews (blind dates on steroids), knowledge quizzes, and milquetoast linkedin-style "networking", are there? Yeah, didn't think so. But I've already written all of this so here you are.
I have a solid history working with spreadsheets, relational databases, rudimentary knowledge of PHP and Python. At a previous employer I designed and programmed custom scripting for multiple variable data printing projects. I was enrolled in Google's "Data Analyst" training on Coursera but unfortunately can't afford the monthly fees to continue.
The caveat is you would need to meet or exceed my current generous retail salary of $15/hr. Try me for a week. If you aren't satisfied, just assassinate me in lieu of payment.
Google released a new repo with a collection of guides and examples for the Gemini API. This includes a set of guides for prompt engineering and examples of the API features 👇🏼
Hands-on Data Science: Complete your First Project 🚀
This beginner crash course by Misra Turp provides an introduction to the foundations of data science by solving real-life examples. This includes the different steps of a data science project, from setting the environment to loading and analyzing the data using Python, git, Jupyter notebooks and other tools 👇🏼
R programmers in The Netherlands, you may be interested in this event with Jenny Bryan about R package development. It is an honour to have her in Utrecht so I hope more people can benefit from this in person opportunity!
This week, PyMC version v5.13.0 was released. PyMC is one of the main #Python 🐍 libraries for 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 statistics ❤️. It provides a framework for probabilistic programming, enabling users to build #Bayesian models with a simple Python API and fit them using 𝐌𝐚𝐫𝐤𝐨𝐯 𝐂𝐡𝐚𝐢𝐧 𝐌𝐨𝐧𝐭𝐞 𝐂𝐚𝐫𝐥𝐨 (MCMC) methods 🚀.
The new release includes new features, bug fixes 🐞, and documentation improvements 📖. More details on the release notes 📝 👇 #DataScience#machinelearning#statistics
Hello #Mastodon! I'm a former practicing lawyer and professor. Looking to chat about #legal issues, #datascience (especially computational text analysis using Python and R 😉 ), and #travelphotography. I care a lot about equitable access to the court system for all peoples, including those who are #neurodiverse, in the US by way of #immigration, #black or people of color.
(1/2) Models Demystified - A Practical Guide from t-tests to Deep Learning 🚀👇🏼
The Models Demystified is a new book by Michael Clark and Seth Berry that focuses on the mechanizing of core data science algorithms. That includes the following topics:
✅ Linear and logistic regression
✅ Generalized Linear Models
✅ Regularization methods
✅ Model training approaches
✅ Deep learning and neural networks
✅ Causal Modeling