▶Book Description
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.
▶What You Will Learn
⦁ Understand the fundamentals of Automated Machine Learning systems
⦁ Explore auto-sklearn and MLBox for AutoML tasks
⦁ Automate your preprocessing methods along with feature transformation
⦁ Enhance feature selection and generation using the Python stack
⦁ Assemble individual components of ML into a complete AutoML framework
⦁ Demystify hyperparameter tuning to optimize your ML models
⦁ Dive into Machine Learning concepts such as neural networks and autoencoders
⦁ Understand the information costs and trade-offs associated with AutoML
▶Key Features
⦁ Build automated modules for different machine learning components Understand each component of a machine learning pipeline in depth Learn to use different open source AutoML and feature engineering platforms
▶Who This Book Is For
If you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You'll also find this book useful if you're an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book.
▶What this book covers
⦁ 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.
