Book Image

Python Machine Learning Cookbook - Second Edition

By : Giuseppe Ciaburro, Prateek Joshi
Book Image

Python Machine Learning Cookbook - Second Edition

By: Giuseppe Ciaburro, Prateek Joshi

Overview of this book

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
Table of Contents (18 chapters)

Identifying the gender of a name

Identifying the gender of a name is an interesting task in NLP. We will use the heuristic that the last few characters in a name is its defining characteristic. For example, if the name ends with la, it's most likely a female name, such as Angela or Layla. On the other hand, if the name ends with im, it's most likely a male name, such as Tim or Jim. As we aren't sure of the exact number of characters to use, we will experiment with this.

Getting ready

In this recipe, we will use the names corpora to extract labeled names, and then we will classify the gender based on the final part of the name.

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