A hands-on guide to automating data and modeling pipelines for faster machine learning applications
- Build automated modules for different machine learning components
- Develop in-depth understanding for each component of a machine learning pipeline
- Learn to use different open source AutoML and feature engineering platforms
AutoML is designed to automate parts of machine learning. The readily available AutoML tools are easing the tasks of Data Science practitioners and are being well-received in the advanced analytics community. This book covers the necessary foundations needed to create automated machine learning modules, and how you can get up to speed with them in the most practical way possible.
You will learn to automate different tasks in the machine learning pipeline such as data pre-processing, feature selection, model training, model optimization and much more. The book also demonstrates you how to use the already available automation libraries such as auto-sklearn and auto-weka, or create and extend your own custom AutoML components for machine learning.
By the end of this book, you will have a clearer understanding of what the different aspects of automated machine learning are, and incorporate the automation tasks using practical datasets. The learning you get from this book can be leveraged to implement machine learning in your projects or get a step closer to win various machine learning competitions.
What you will learn
- Understand the fundamentals of Automated Machine Learning systems
- Explore auto-sklearn and auto-weka for AutoML tasks
- Automate your pre-processing methods along with feature transformation
- Enhance feature selection and generation using the Python stack
- Join all of the individual components into a complete AutoML framework
- Demystify hyperparameter tuning to use them to optimize your ML models
- Dive into concepts such as neural networks and autoencoders
- Understand the information costs and trade-offs associated with AutoML
Who This Book Is For
This book is ideal for budding data scientists, data analysts and machine learning enthusiasts who are new to the concept of automated machine learning. ML engineers and data professionals who are interested in developing quick machine learning pipelines for their projects will also find this book to be useful. Prior exposure to Python programming is required to get the best out of this book.
Table of Contents
Chapter 1. Packt Upsell
Chapter 2. Contributors
Chapter 3. Preface
Chapter 4. Introduction to AutoML
Chapter 5. Introduction to Machine Learning Using Python
Chapter 6. Data Preprocessing
Chapter 7. Automated Algorithm Selection
Chapter 8. Hyperparameter Optimization
Chapter 9. Creating AutoML Pipelines
Chapter 10. Dive into Deep Learning
Chapter 11. Critical Aspects of ML and Data Science Projects