@Frawzey@becausealice2 thanks for the good answers. @erictleung that's a good advice and as we "speak" I'm trying to developed some simple ML to get a better understanding of it, but needed some basic knowledge to get started 😁, if I succeed I will uploaded it to my GitHub. Again thanks for helping a n00b 😃
@jkielsgaard yes, please share! It can be scary sharing your stuff and putting it online, but I think you get the opportunity for some constructive feedback from people who want to help. Good luck! And to add to just "build things", I'd add that it is important to reflect on your lessons learned and stumbling blocks you might have learned along the way. These sorts of insights are rarely remembered from experts trying to teach students.
@jkielsgaard building things also gives you a measuring stick of the progress you've made in learning. Just writing notes doesn't give you anything concrete to look back on show that you've learned something. BUT if your notes are shared and bring value to people, that also helps! Example: https://github.com/Avik-Jain/100-Days-Of-ML-Code
@pdurbin nice! Skimming through that discussion, there appears to be some kinks (e.g. pointing to Kaggle rather than the original Stanford data). But it does seem to search across everything, specifically for datasets. We'll see how it develops!