The goal of this group is to help you navigate the complex and rapidly evolving world of data science and artificial intelligence. This is your hub to stay up-to-date on the latest trends, learn specialized skills to turn raw data into valuable insights, connect with a community of like-minded individuals, and ultimately, become a Data Alchemist. Together, let's decode the language of data and shape a future where knowledge and community illuminate our way. Rules - Don't sell anything here or use Data Alchemy as any kind of funnel - We delete low effort community posts, and posts with poor English. Proofread your post first. - Help us make the posts high quality. If you see a low quality post, then click on the 3 dots on the post and "Report To Admins". Start by checking out these links - Classroom - Introduction - Roadmap - Contribution Be Aware of Scammers - Please be aware that this is a public group. Unfortunately, some people abuse the Skool platform to send DMs or post comments to trick people. This is the internet, so always do your own due diligence. Never automatically trust someone here on the Skool platform other than @Dave Ebbelaar's official account. To kick things off, please comment below, introducing yourself. Let us know: 1. Your name and where you're from 2. What project(s) you're currently focused on See you in the comments!

Hi, guys! How's it going? Today, I'm sharing an interesting article about the need for AI democratization. This article talks about Huggingface CEO Clément Delangue and says that he believes that AI development power should not be concentrated in the hands of a few large tech companies. The cool thing is that Huggingface is making AI more democratic by developing an open-source platform where any developer or startup can access AI models for free. Their approach contrasts with companies like OpenAI, Meta, and Google, which keep their models proprietary. Delangue advocates for more transparency about how AI systems are built and their potential societal impacts. Huggingface strives to empower communities of developers through collaboration and knowledge sharing. While huge tech firms invest in Huggingface, Delangue still critiques their secrecy around AI. With over 50,000 organizations using its services, Huggingface is positioning itself as a GitHub for AI to drive more cooperation in how this technology progresses. Check out the article here: link to article Happy learning everyone!

I've been working with Large Language Models (LLMs) for a year now. Creating apps for businesses ranging from SME's to Enterprises. Here are 5 lessons I learned in 2023: 1. Quality data is still the key to success 2. Start with internally-facing applications (lower risk) 3. Transitioning from PoC to production is challenging 4. Implement LLM Operations (LLMOps) from day one 5. Be prepared to change your architecture every quarter What's your biggest lesson from learning or working with AI this year? P.S. Are we already connected on LinkedIn?

If, like me, you are not a data expert, you might like to know that the pandas module has a little brother: pandas-ai. so whereas with pandas, you analyse your data like so: ``` males = df[df["Sex"] == "male"] male_survival = males["Survived"].sum()/len(males) females = df[df["Sex"] == "female"] female_survival = females["Survived"].sum()/len(females) female_survival/male_survival ``` to find out how much more likely females were to survive the Titanic disaster (fyi, 3.93 times). with pandas.ai, you would ask your friendly LLM directly: ``` pandas_ai.chat("what sex was most likely to have survived? how much more likely?") ``` 'The sex most likely to have survived is female, and they were 3.93 times more likely to have survived compared to the other sex.'

For some individuals, you may want to run a GPT on your local machine for a number of reasons. 1) You can't pay for or acquire a chatGPT membership 2) You may need to sometimes work offline 3) You are working on a super secret business idea and you don't want a corporation on the other end There are at least a couple of options you can run locally, they in turn allow a choice of open source LLMs from Hugging Face. Just note this requires a fairly recent machine for decent performance, but not a GPU. A good open source LLM is Falcon. Here are some free options. LM Studio https://lmstudio.ai/ GPT4ALL https://gpt4all.io/index.html?ref=dataphoenix.info Hugging Face (for LLMs) https://huggingface.co/ Hope this is useful to someone. 🙂