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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.
Table of Contents (18 chapters)
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16
Other Books You May Enjoy
17
Index

Recognizing Faces with Support Vector Machine

In the previous chapter, we discovered underlying topics using clustering and topic modeling techniques. This chapter continues our journey of supervised learning and classification, with a particular emphasis on Support Vector Machine (SVM) classifiers.

SVM is one of the most popular algorithms when it comes to high-dimensional spaces. The goal of the algorithm is to find a decision boundary in order to separate data from different classes. We will discuss in detail how that works. Also, we will implement the algorithm with scikit-learn and apply it to solve various real-life problems, including our main project of face recognition. A dimensionality reduction technique called principal component analysis, which boosts the performance of the image classifier, will also be covered in this chapter, as will support vector regression.

This chapter explores the following topics:

  • Finding the separating boundary with SVM
  • ...
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Python Machine Learning By Example
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