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Book details
Paperback: 154 pages
Format: pdf
Size: 17.54 MB
Publisher: Independently Published (8 May 2019)
Language: English
ISBN-10: 1097417115
ISBN-13: 978-1097417117
About the book
This book is written for sophomore students in computer science, technology, engineering, or mathematics (STEM), assuming that they know algebra and calculus. Readers should have already solved some problems using computer programs. More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into.
This book is to provide a conceptual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical training.
More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into. The book discusses many methods that have their bases in different fields: statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. In the past, research in these different communities followed different paths with different emphases. In this book, the aim is to incorporate them together to give a unified treatment of the problems and the proposed solutions to them. This is an introductory textbook, intended for senior undergraduate and graduate-level courses on machine learning, as well as engineers working in the industry who are interested in the application of these methods.
Book details
Paperback: 154 pages
Format: pdf
Size: 17.54 MB
Publisher: Independently Published (8 May 2019)
Language: English
ISBN-10: 1097417115
ISBN-13: 978-1097417117
About the book
This book is written for sophomore students in computer science, technology, engineering, or mathematics (STEM), assuming that they know algebra and calculus. Readers should have already solved some problems using computer programs. More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into.
This book is to provide a conceptual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical training.
More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into. The book discusses many methods that have their bases in different fields: statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. In the past, research in these different communities followed different paths with different emphases. In this book, the aim is to incorporate them together to give a unified treatment of the problems and the proposed solutions to them. This is an introductory textbook, intended for senior undergraduate and graduate-level courses on machine learning, as well as engineers working in the industry who are interested in the application of these methods.