Hey data alchemists! I've been having a dilemma recently with a section in the "Data Science Accelerator" module and that dilemma is in Feature Engineering. I had a hard time with understanding this topic and don't know what to do. Do I skip it and come back to it or do I have to study it because the other things are built around it in the module? I'd love to hear your opinions about this. Thanks in advance!

Hi everyone! I am almost finished with my python courses, and I am feeling very confident on my journey. I want to dive into ai and machine learning, but I dont know whereto start, could you all guide me!

Hey Data Alchemists 👋🏻 I hope you all are doing well and progressing on your goals and objectives toward your futures Currently I am having a dilemma. I've recently finished the fundamental ML skills and can't find good datasets to try out what I've learned. Any suggestions for datasets you have worked with could help me a ton with practicing my newfound skills in ML. Thanks in advance 🙏🏻

Hi everyone, I'm excited to start learning about AI & ML! However, the abundance of information online is overwhelming. Some sources stress the importance of linear algebra, while others suggest alternative paths. Adding to the confusion, bootcamp costs can be quite high. Therefore, I'm reaching out to the community for recommendations on high-quality, free, or low-cost learning materials. Ideally, these resources would provide a comprehensive introduction to AI/ML and be well-organized to avoid information overload. Any insights or suggestions you might have on building a solid learning foundation would be greatly appreciated! Thanks in advance for your help!

Hi, I’m Keith and i’m looking forward to getting started.