Automated Machine Learning in Action

2 The end-to-end pipeline of an ML project

This chapter covers

• Getting familiar with the end-to-end pipeline for conducting an ML project
• Preparing data for ML models (data collection and preprocessing)
• Generating and selecting features to enhance the performance of the ML algorithm
• Building up linear regression and decision tree models
• Fine-tuning an ML model with grid search

Now that the first chapter has set the scene, it’s time to get familiar with the basic concepts of ML and AutoML. Because AutoML is grounded in ML, learning the fundamentals of ML will help you better understand and make use of AutoML techniques. This is especially the case when it comes to designing the search space in an AutoML algorithm, which characterizes the ML ...