Getting started. Two of the most de-motivational words in the English language. The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating.
Where to begin?
This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? Which complement one another? What is the best order in which to use selected resources?
Moving forward, I make the assumption that you are not an expert in:
▪ Machine learning
▪ Any of Python’s machine learning, scientific computing, or data analysis libraries
It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won’t be necessary; some extra time spent on the earlier steps should help compensate.