Sibanjan Das - Hands-On Automated Machine Learning

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Sibanjan Das is a business analytics and data science consultant. He has extensive experience in implementing predictive analytics solutions in business systems and IoT. An enthusiastic and passionate professional about technology and innovation, he has loved wrangling with data since the early days of his career. He has a master's in IT with a major in business analytics from Singapore Management University, and holds several industry certifications such as OCA, OCP, an d CSCMS.

Umit Mert Cakmak is a Data Scientist at IBM, where he excels at helping clients to solve complex data science problems, from inception to delivery of deployable assets. His research spans across multiple disciplines beyond his industry and he likes sharing his insights at conferences, universities, and meet-ups.

Brian T. Hoffman has developed and deployed data science solutions for 20 years, in fields such as drug discovery, biotech, software, and sales. After obtaining his PhD in drug discovery from University of North Carolina, Chapel Hill, he completed his postdoctoral fellowship in developing new ML techniques with the National Institutes of Health. He has a passion for determining how data can help improve business decisions, and has managed international teams of scientists implementing data science solutions for companies ranging from startups to Fortune 100.

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Dear reader, welcome to the world of automated machine learning (ML). Automated ML (AutoML) is designed to automate parts of ML. The readily available AutoML tools make the tasks of data science practitioners easier and are being well received in the advanced analytics community. This book covers the foundations you need to create AutoML modules, and shows how you can get up to speed with them in the most practical way possible.

You will learn to automate different tasks in the ML pipeline, such as data preprocessing, feature selection, model training, model optimization, and much more. The book also demonstrates how to use already available automation libraries, such as auto-sklearn and MLBox, and how to create and extend your own custom AutoML components for ML.

By the end of this book, you will have a clearer understanding of what the different aspects of AutoML are, and will be able to incorporate the automation tasks using practical datasets. The knowledge you get from this book can be leveraged to implement ML in your projects, or to get a step closer to winning an ML competition. We hope that everyone who buys this book finds it worthy and informative.

This book is ideal for budding data scientists, data analysts, and ML enthusiasts who are new to the concept of AutoML. Machine learning engineers and data professionals who are interested in developing quick machine learning pipelines for their projects will also find this book useful.