Preparing for ML Development
In Part 2 of the book, we will examine the ML process. We will start from the preparation work, which includes ML problem framing to define an ML problem; data preparation and feature engineering to get the data ready; followed by the ML model development phases, which include model training, model validation, model testing, and model deployment. We will end Part 2 with neural networks and DL.
In this chapter, will discuss the two ML preparation tasks: ML problem framing and data preparation. We will address the following questions for the problem we are solving:
- What are the business requirements?
- Is ML the best way to solve the problem?
- What are the inputs and outputs for the problem?
- Where is my data?
- How do I measure the success of the ML solution?
- Is the data ready?
- How do I collect my data?
- How do I transform and construct my data?
- How do I select features for the ML model?
It is very important that...
