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.
![Using Asyncio in Python [Understanding Python’s Asynchronous Programming Features]](https://www.rulit.me/data/programs/images/using-asyncio-in-python-understanding-python-s-asynchronous_606937.jpg "Python 3.4 introduced the asyncio library, and Python 3.5 produced the async and await keywords to use it palatably. These new additions allow so-called asynchronousprogramming.
All of these new features, which I’ll refer to under the single name Asyncio, have been received by the Python...")
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