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Python Machine Learning By Example

Python Machine Learning By Example - Fourth Edition

By : Yuxi (Hayden) Liu
4.9 (8)
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Python Machine Learning By Example

Python Machine Learning By Example

4.9 (8)
By: Yuxi (Hayden) Liu

Overview of this book

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.
Table of Contents (18 chapters)
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16
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Index

Getting started with two types of data – numerical and categorical

At first glance, the features in the preceding dataset are categorical – for example, male or female, one of four age groups, one of the predefined site categories, and whether the user is interested in sports. Such data is different from the numerical feature data we have worked with until now.

Categorical features, also known as qualitative features, represent distinct characteristics or groups with a countable number of options. Categorical features may or may not have a logical order. For example, household income from low to medium to high is an ordinal feature, while the category of an ad is not ordinal.

Numerical (also called quantitative) features, on the other hand, have mathematical meaning as a measurement and, of course, are ordered. For instance, counts of items (e.g., number of children in a family, number of bedrooms in a house, and number of days until an event ) are discrete numerical...

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Python Machine Learning By Example
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