Automated Machine Learning in Action - Qingquan Song, Xia Hu, Haifeng Jin

Automated Machine Learning in Action is a perfect guide introducing AutoML and showing how it can be implemented through AutoKeras and KerasTuner. It helps the readers learn basic mechanisms to optimize and enhance all the phases of their machine-learning processes. AutoML also minimizes errors in hyperparameter tuning, selection of pipeline components, and model optimization, hence saving time for data scientists. Some sections may be skipped by professionals due to the basic and easy-to-read approach of the book and the focus on concrete samples of using AutoML approaches.

AutoML is performed using pre-defined solutions to complicated machine learning procedures like data preparation and actual model selection. AutoML entails standardization of work across the different stages of the ML process and removing the heavy lifting in repetitive tasks; hence, users, especially those with less exposure to machine learning, can apply them conveniently while at the same time freeing up time for specialists. In “Automated Machine Learning in Action,” one will learn how AutoKeras and KerasTuner help automate classification, regression, data augmentation, and many other tasks to develop an ML System that can self-tune with very little human involvement.

Being more focused on the methods that have evidential value, the book describes how AutoML helps speed up the ML process and, thus, enhances the outcomes. As a result the readers would be to learn how to optimize the pipeline components without actually struggling to do so, tuning of hyperparameters of the model, different search algorithms as well as the acceleration of search would be made available to the readers. Going deeper into the details of AutoML, “Automated Machine Learning in Action” provides the readers with the knowledge and tools for unleashing the potential of automated machine learning for any given project regardless of the experience level of the project members.

Automated Machine Learning in Action Table of Contents:

1. Part 1. Fundamentals of AutoML
2. Chapter 1. From machine learning to automated machine learning
3. Chapter 2. The end-to-end pipeline of an ML project
4. Chapter 3. Deep learning in a nutshell
5. Part 2. AutoML in practice
6. Chapter 4. Automated generation of end-to-end ML solutions
7. Chapter 5. Customizing the search space by creating AutoML pipelines
8. Chapter 6. AutoML with a fully customized search space
9. Part 3. Advanced topics in AutoML
10. Chapter 7. Customizing the search method of AutoML
11. Chapter 8. Scaling up AutoML
12. Chapter 9. Wrapping up
13. Appendix A. Setting up an environment for running code
14. Appendix B. Three examples: Classification of image, text, and tabular data

Who is this course for?

1. Beginners entering machine learning.
2. Engineers of different levels in search of workflow automation.
3. The founders and managers who want to maximize pipelines using AutoKeras and KerasTuner.
4. Anyone who wants to do something like hyperparameter tuning and you want to automate the process.
5. People researching how to enhance generative models and other types of Machine Learning techniques.
6. Readers seek real-life examples and/or simplified/non-technical language to discuss terminology and concepts.

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