I am new in this area of Machine Learning and Neural Networks. Currently, I'm taking some courses on Udemy and reading a book about it, but I still have one big question regarding data pre-processing.
In all of those Udemy's lessons, people always use a perfect dataset and ready to input in a model. So all you have to do is run it.
How do I know if my dataset is ready for a model? What do I have to do to make it ready? Which evaluations?
I had a few statistics classes in college already and I learned a lot about correlations matrices, autocorrelation functions, and its lags, etc. and I didn't see yet in anywhere someone explaining how can I evaluate my data and then proceed to implement a model to solve my problem.
If anyone could point me a direction, give me some material, show me where I can learn this, anything, it would be really helpful!