Applied Machine Learning with Python

Andrea Giussani

ISBN 10: 8831322044 / ISBN 13: 9788831322041

Published by Egea Spa - Bocconi University Press, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - If you are looking for an engaging book, rich in learning features, which will guide you through the field of Machine Learning, this is it. This book is a modern, concise guide of the topic. It focuses on current ensemble and boosting methods, highlighting contemporray techniques such as XGBoost (2016), Shap (2017) and CatBoost (2018), which are considered novel and cutting edge models for dealing with supervised learning methods. The author goes beyond the simple bag-of-words schema in Natural Language Processing, and describes the modern embedding framework, starting from the Word2Vec, in details. Finally the volume is uniquely identified by the book-specific software egeaML, which is a good companion to implement the proposed Machine Learning methodologies in Python.

Seller Inventory # 9788831322041

Bibliographic Details

• Title: Applied Machine Learning with Python
• Publisher: Egea Spa - Bocconi University Press
• Publication Date: 2020
• Language: English
• ISBN 10: 8831322044
• ISBN 13: 9788831322041
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 241W x 171H x 11D millimeters 241 millimeters width by 171 millimeters height by 11 millimeters depth
• Weight: 356 grams

About this title

Synopsis

If you are looking for an engaging book, rich in learning features, which will guide you through the field of Machine Learning, this is it. This book is a modern, concise guide of the topic. It focuses on current ensemble and boosting methods, highlighting contemporray techniques such as XGBoost (2016), Shap (2017) and CatBoost (2018), which are considered novel and cutting edge models for dealing with supervised learning methods. The author goes beyond the simple bag-of-words schema in Natural Language Processing, and describes the modern embedding framework, starting from the Word2Vec, in details. Finally the volume is uniquely identified by the book-specific software egeaML, which is a good companion to implement the proposed Machine Learning methodologies in Python.

About the Author

Andrea Giussani is an Academic Fellow in Computer Science at Bocconi University. He holds a PhD in Statistics, and he has published in several peer-reviewed journals.