Book Image

Hands-On Automated Machine Learning

By : Sibanjan Das, Umit Mert Cakmak
Book Image

Hands-On Automated Machine Learning

By: Sibanjan Das, Umit Mert Cakmak

Overview of this book

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.
Table of Contents (10 chapters)

Summary

This chapter was a sketch of building pipelines for ML systems—it is just the tip of the iceberg. Building pipelines is very complicated. However, once developed, it makes the life of a developer more comfortable. It reduces the complexity of formulating different models and thus becomes an essential concept, which is required to create an AutoML system. The concepts that we described in this chapter give you a foundation for creating pipelines. You must have understood when you built the pipelines in this chapter how well-structured the model building process became with the use of pipelines.

The next chapter will summarize our learning so far. It will also provide you with several suggestions that would be useful in devising an AutoML system and executing data science projects successfully.