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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
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Index

Exploring Naïve Bayes

The Naïve Bayes classifier belongs to the family of probabilistic classifiers. It computes the probabilities of each predictive feature (also referred to as an attribute or signal) of the data belonging to each class in order to make a prediction of the probability distribution over all classes. Of course, from the resulting probability distribution, we can conclude the most likely class that the data sample is associated with. What Naïve Bayes does specifically, as its name indicates, is as follows:

  • Bayes: As in, it maps the probability of observed input features given a possible class to the probability of the class given observed pieces of evidence based on Bayes’ theorem.
  • Naïve: As in, it simplifies probability computation by assuming that predictive features are mutually independent.

I will explain Bayes’ theorem with examples in the next section.

Bayes’ theorem by example

It is important...

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Python Machine Learning By Example
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