awesome-data-centric-ai VS ydata-synthetic

I would finish the degree anyway. It's only one year left. If teachers miss classes, I would disregard that and try to learn on my own, and then yes, I would move on to an internship (or even do It at the same time if it's possible). If you like, come as meet us at the Data-Centric AI Community and we can do some projects together :)

Definitely a lot of growth in the AI space, and it will evolve rapidly in the next few years. There several paid propositions at the Data-Centric AI Community discord, check them out.

2: Tbh, quit (?) We have open jobs at the Data-Centric AI Community. Bonus points: you can vent there as much as you want

Let me invite you to the Data-Centric AI Community we have several code along sessions and projects and a lot of beginners that are starting to learn DS that you can connect with.

If you need specific help with your project you can find me at the Data-Centric AI Community and we'll be happy to take a look and give you some tips to move forward :)

You can share with us your progress on the Data-Centric AI Community and ask someone to review it, we often do that with CVs as well and help each other out.

Maybe you can use a synthetic data generator and use your current dataset as input? I believe there are a lot of GAN-based models for this purpose out there. The ones listed on https://github.com/Data-Centric-AI-Community/awesome-data-centric-ai are mostly focused on structured data, but I'm sure there are similar packages for images.

Send us your North ⭐️: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! 🎵 If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!

1: Try to work with what you have and augment your dataset (honestly, 10 points is crap)

Data Quality is key for all applications and models, and LLMs are no exception :) I've been working on a small community project with synthetic data (https://github.com/ydataai/ydata-synthetic) using ydata-synthetic, and it really shows! Underrepresentation (category imbalance) and missing data are two of the main issues!

The Data-Centric AI Community creates community projects from time to time and is probably willing to help you in your project.

Join nice data communities and start networking.

You know what they say: "Tell me who your friends are, and I'll tell you who you are!". Hang out with DS beasts and learn from them :)

Interesting question! I think our AI/ML devs at the Data-Centric AI Community could have nice perspectives for your to decide :)

Sounds like an amazing journey! Feel free to add your projects on our awesome-python-for-data-science repo as you go! And in case you need a hand or feedback on the projects, we'll be happy to help at the Data-Centric AI Community.

The best to do is to get started with end-to-end projects in a collaborative environment (somewhat approaching real-world settings). You may find some interesting resources in this GitHub repository. The Data-Centric AI Community actually has a nice support system for this.