Аннотация
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.
![Cracking the Coding Interview: 189 Programming Questions and Solutions [6th Edition]](https://www.rulit.me/data/programs/images/cracking-the-coding-interview-189-programming-questions-and_491615.jpg "I am not a recruiter. I am a software engineer. And as such, I know what it")
![HTML5 and CSS3 All-in-One For Dummies® [3rd Edition]](https://www.rulit.me/data/programs/images/html5-and-css3-all-in-one-for-dummies0-3rd-edition_481775.jpg "A new edition of a bestseller covers the latest advances in web development!
HTML5 and CSS3 are essential tools for creating dynamic websites and boast updates and enhanced features that can make your websites even more effective and unique. This friendly, all-in-one guide covers everything you...")
Комментарии к книге "Applied machine learning with python"