This checklist can guide you through your Machine Learning projects. There are eight main steps:
Frame the problem and look at the big picture.
Get the data.
Explore the data to gain insights.
Prepare the data to better expose the underlying data patterns to Machine Learning algorithms.
Explore many different models and short-list the best ones.
Fine-tune your models and combine them into a great solution.
Present your solution.
Launch, monitor, and maintain your system.
Obviously, you should feel free to adapt this checklist to your needs.
Define the objective in business terms.
How will your solution be used?
What are the current solutions/workarounds (if any)?
How should you frame this problem (supervised/unsupervised, online/offline, etc.)?
How should performance be measured?
Is the performance measure aligned with the business objective?
What would be the minimum performance needed to reach the business objective?
What are comparable problems? Can you reuse experience or tools?
Is human expertise available?
How would you solve the problem manually?
List the assumptions you (or others) have made so far.
Verify assumptions if possible.
Note: automate as much as possible so you can easily get fresh data.
List the data you need and how much you need.
Find and document where you can get that data.
Check how much space it ...