7. Intro to Machine Learning¶
Within the general process of modeling, this chapter focuses primarily on what we do after we define the problem, think about the question and data, and get the data.
Start with an interesting question or problem
What type of question are you asking?
Think about data: […]
After step 3: Actually develop your ML model. That’s this chapter.
The workflow for the actual development of an ML model is:
This chapter will discuss this modeling process backwards and will mostly focus on “supervised” prediction problems.
Working backwards will help keep our focus on why we are doing certain steps, rather than getting mired in the weeds, which can lead to poor or disastrous analysis.
After discussing the issues in the subchapters here, the next chapter will get into coding.
“Machine learning, explained”, which provides a nice overview of ML.
PythonDataScienceHandbook has a nice introductory list of example applications for ML techniques
sklearnexample page has a rather comprehensive set of examples with code!