Applied Machine Learning with Python

✍ Scribed by Andrea Giussani

• Publisher: Bocconi University Press
• Year: 2020
• Tongue: English
• Leaves: 204
• Category: Library

✦ 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.

✦ Table of Contents

APPLIED MACHINE LEARNING WITH PYTHON
Contents
List of Figures
Preface
Chapter 1. Introduction to Machine Learning
1.1 A simple supervised model: Nearest Neighbor
1.2 Preprocessing
1.3 Methods for Dealing with Imbalanced Data
1.4 Reducing Dimensionality: Principal Component Analysis
Chapter 2. Linear Models for Machine Learning
2.1 Linear Regression
2.2 Shrinkage Methods
2.3 Robust Regression
2.4 Logistic Regression
2.5 Linear Support Vector Machine
2.6 Beyond Linearity: Kernelized Models
Chapter 3. Beyond Linearity: Ensemble Methods for Machine Learning
3.1 Introduction
3.2 Ensemble Methods
3.3 Random Forests
3.4 Boosting Methods
Chapter 4. An Introduction to Modern Machine Learning Techniques
4.1 Introduction to Natural language Processing
4.2 Introduction to Deep Learning
Appendices
Appendix A. A crash course in Python
A.1 Building Blocks in Python
A.2 Data Structure in Python
A.3 Loops in Python
A.4 Advanced Data Structure in Python
A.5 Advanced Concepts on Functions
A.6 Introduction to Object-Oriented Programming
Appendix B. Mathematics behind the skip-gram model
Index
Bibliography
Back Cover