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040    CtWfDGI|beng|erda|cCtWfDGI 
100 1  Giussani, Andrea,|d1965-|eauthor. 
245 10 Applied Machine Learning with Python /|cAndrea Giussani. 
264  1 [Place of publication not identified] :|bEGEA Spa - 
       Bocconi University Press,|c[2020] 
264  4 |c©2020 
300    1 online resource (200 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file|2rdaft 
347    |bPDF 
506    Access limited to subscribing institutions. 
520    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. 
588 0  Publisher metadata. 
650  7 COMPUTERS / Programming Languages / Python.|2bisacsh 
655  0 Electronic books. 
914    frd00033793