User Question: I’m just now beginning to learn AI, but don’t really know much about it. What would be the best way for me to learn?
Our premise is that, if you have data then you can be a data scientist or, what we call, a citizen data scientist in the sense that if you have data and know the questions that you want to ask with this data, then you have everything you need to get started. MindsDB is meant for you to actually build machine learning models through MindsDB and get explanations from it. It tries to change the paradigm from being someone who knows AI or being someone who learns machine learning to being someone who understands what the important questions to answer and what data they should get to answer those specific questions. The machine learning framework and explainability are there for you when you can get to that place where you understand the questions you need to ask and the data you need to answer those questions.
As you get more confident with that part then you can start diving deeper into how these models are being built. We expose this to you through the fact that MIndsDB is open source and that we have a framework that allows you to dive deep into how this happens, which is MindsDB Lightwood.
So the advice I have for someone who is just starting is to first try to think of or find a data set that is close to you. There are many places, like Kaggle, to find data sets that are interesting to what you or to what your domain expertise is. After you have a data set, try MindsDB. Try to from the MindsDB Graphical User Interface, Scout, to the native model that we have–the python one, and then once you feel comfortable with what you’re getting there (also feel free to like provide feedback and let us know the things that don’t make sense because again we want to make sure that this is a suitable solution for people that have some data). As you progress down that line then–because we want everyone to understand what’s happening inside of MindsDB–you can go into MindsDB Lightwood and try to change the weights of how some of the vector representations or how these mixers are being built.