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Applied Machine Learning with Python
Applied Machine Learning with Python

Applied Machine Learning with Python


200 Pages, 6.75 x 9.5

Formats: PDF, Trade Paper

Trade Paper, $39.95 (US $39.95) (CA $46.95)

Publication Date: March 2020

ISBN 9788831322041

Rights: WOR X IT

EGEA Spa - Bocconi University Press (Mar 2020)

Out of Stock. E-book edition is available.


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.

Author Biography

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.