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Python Machine Learning By Example

Python Machine Learning By Example - Fourth Edition

By : Yuxi (Hayden) Liu
4.3 (25)
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Python Machine Learning By Example

Python Machine Learning By Example

4.3 (25)
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. *Email sign-up and proof of purchase required
Table of Contents (18 chapters)
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16
Other Books You May Enjoy
17
Index

Getting started with classification

Movie recommendation can be framed as a machine learning classification problem. If it is predicted that you’ll like a movie because you’ve liked or watched similar movies, for example, then it will be on your recommended list; otherwise, it won’t. Let’s get started by learning the important concepts of machine learning classification.

Classification is one of the main instances of supervised learning. Given a training set of data containing observations and their associated categorical outputs, the goal of classification is to learn a general rule that correctly maps the observations (also called features or predictive variables) to the target categories (also called labels or classes). Putting it another way, a trained classification model will be generated after the model learns from the features and targets of training samples, as shown in the first half of Figure 2.1. When new or unseen data comes in, the trained...

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Python Machine Learning By Example
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