Though classical approaches to such tasks exist, and have existed for some time, it is worth taking consult from new and different perspectives for a variety of reasons: Have I missed something? Are there new approaches which had not previously been considered? Should I change my perspective on how I approach machine learning?

The 2 most recent resources I've come across outlining frameworks for approaching the process of machine learning are Yufeng Guo's The 7 Steps of Machine Learning and section 4.5 of Francois Chollet's Deep Learning with Python. Are either of these anything different than how you already process just such a task?




The 7 Steps Of Machine Learning