This book is a modern, concise guide on the field of Machine Learning. It focuses on current ensemble and boosting methods, highlighting contemporary techniques such as XGBoost (2016), Shap (2017) and CatBoost (2018), which are considered novel and cutting edge algorithms for dealing with supervised learning methods. The author goes beyond the simple bag-of-words schema in Natural Language Processing, and describes modern embedding frameworks, starting from 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.
Applied machine learning with Python
Andrea Giussani
2020
Abstract
This book is a modern, concise guide on the field of Machine Learning. It focuses on current ensemble and boosting methods, highlighting contemporary techniques such as XGBoost (2016), Shap (2017) and CatBoost (2018), which are considered novel and cutting edge algorithms for dealing with supervised learning methods. The author goes beyond the simple bag-of-words schema in Natural Language Processing, and describes modern embedding frameworks, starting from 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.
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