Applied Machine Learning with Python

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.

Table of Contents

Chapter 1. Introduction to Machine Learning

Chapter 2. Linear Models for Machine Learning

Chapter 3. Beyond Linearity: Ensemble Methods for Machine Learning

Chapter 4. An Introduction to Modern Machine Learning Techniques Appendices

Appendix A. A crash course in Python

Appendix B. Mathematics behind the skip-gram model

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.