Chapter 1, Introduction to AutoML, creates a foundation for you to dive into AutoML. We also introduce you to various AutoML libraries.
Chapter 2, Introduction to Machine Learning Using Python, introduces some machine learning concepts so that you can follow the AutoML approaches easily.
Chapter 3, Data Preprocessing, provides an in-depth understanding of different data preprocessing methods, what can be automated, and how to automate it. Feature tools and auto-sklearn preprocessing methods will be introduced here.
Chapter 4, Automated Algorithm Selection, provides guidance on which algorithm works best on which kind of dataset. We learn about the computational complexity and scalability of different algorithms, along with methods to decide the algorithm to use based on training and scoring time. We demonstrate auto-sklearn and how to extend it to include new algorithms.
Chapter 5, Hyperparameter Optimization, provides you with the required fundamentals on automating hyperparameter tuning a for variety of variables.
Chapter 6, Creating AutoML Pipelines, explains stitching together various components to create an end-to-end AutoML pipeline.
Chapter 7, Dive into Deep Learning, introduces you to various deep learning concepts and how they contribute to AutoML.
Chapter 8, Critical Aspects of ML and Data Science Projects, concludes the discussion and provides information on various trade-offs on the complexity and cost of AutoML projects.
