Inspired by Aurélien Géron's Hands-On Machine Learning with Scikit-Learn & TensorFlow and my work as a consultant in IBM, I will try to make my Python package Gossipcat as a tool kit for machine learning consulting project. In this document, I will use Aurélien's checklist as a start point and go further to realize it in GossipCat; for those that cannot be generalized in code, I will give my advice based on my consulting expeirence and my own experience like the one from algorithm competitions.
- 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.