Hands-On Automated Machine Learning : A Beginner's Guide to Building Automated Machine Learning Systems Using AutoML and Python

**A hands-on guide to automating data and modeling pipelines for faster machine learning applications** Key Features* 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 Book DescriptionAutoML 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 ForThis 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. A hands-on guide to automating data and modeling pipelines for faster machine learning applications Key Features 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 Book Description 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. Automate data and model pipelines for faster machine learning applications Key FeaturesBuild automated modules for different machine learning componentsUnderstand each component of a machine learning pipeline in depthLearn to use different open source AutoML and feature engineering platformsBook DescriptionAutoML 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 learnUnderstand the fundamentals of Automated Machine Learning systemsExplore auto-sklearn and MLBox for AutoML tasksAutomate your preprocessing methods along with feature transformationEnhance feature selection and generation using the Python stackAssemble individual components of ML into a complete AutoML frameworkDemystify hyperparameter tuning to optimize your ML modelsDive into Machine Learning concepts such as neural networks and autoencodersUnderstand the information costs and trade-offs associated with AutoMLWho this book is forIf 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. This book helps machine learning professionals in developing AutoML systems that can be utilized to build ML solutions. This book covers the necessary foundations and shows the most practical ways possible to get to speed with regards to creating AutoML modules.