Machine Learning Life Cycle
Machine learning modeling in practice, either at the industrial level or in academic research, is beyond writing a couple of lines of Python code to train and evaluate a model on a public dataset. Learning to write a piece of Python program to train a machine learning model using Python and scikit-learn or a deep learning model using PyTorch is a starting point for becoming a machine learning developer and specialist. In this chapter, you will learn about the components of the machine learning life cycle and how, while considering this life cycle when planning for machine learning modeling, it helps you in designing a valuable and scalable model.
Here are the topics, including the main components of the machine learning life cycle, that will be covered in this chapter:
- Before we start modeling
- Data collection
- Data selection
- Data exploration
- Data wrangling
- Modeling data preparation
- Model training and evaluation
- Testing...
