Cleaning the data, compare the models and understand why the results are like they are, those are huge things in Machine Learning. Actually it is like 90% of my job. Training the model it is nothing compared to it. As I said in another comment, I have seen people doing so many mistakes before training or comparing the models. They spent weeks seeing the models with good results in their test but performing like a random classifier in production. Just because they training setup was wrong, they didn't know how to compare models, etc. Machine learning is not like learning a new framework. You can learn the framework and use it, but you are going to do so many mistakes because all the other machine learning knowledge you need.