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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.
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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.
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Applied Machine Learning With Python
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Applied Machine Learning with Python
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Unlock the power of machine learning with Python. This practical guide provides a concise yet comprehensive introduction to applying machine learning techniques using Python. Ideal for data scientists, machine learning engineers, and students, this resource covers fundamental concepts and modern algorithms, including ensemble methods like XGBoost and CatBoost.Learn to tackle real-world challenges with techniques for handling imbalanced data, dimensionality reduction, and feature engineering. Benefit from clear explanations, practical code examples, and a focus on actionable insights. Transform your data into knowledge and drive better decisions. This book empowers you to build and deploy effective machine learning solutions. Master essential machine learning algorithms Apply practical techniques for data preprocessing Implement ensemble methods for improved accuracy. Seller Inventory # 9788831322041
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