As promised, in this chapter, we will kick off our supervised learning journey with machine learning classification, and specifically, binary classification. The goal of the chapter is to build a movie recommendation system, which is a good starting point for learning classification from a real-life example—movie streaming service providers are already doing this, and we can do the same.

In this chapter, you will learn the fundamental concepts of classification, including what it does and its various types and applications, with a focus on solving a binary classification problem using a simple, yet powerful, algorithm, Naïve Bayes. Finally, the chapter will demonstrate how to fine-tune a model, which is an important skill that every data science or machine learning practitioner should learn.

We will go into detail on the following topics:

Getting started with classification
Exploring...

Implementing Naïve Bayes

After calculating the movie preference example by hand, as promised, we are going to implement Naïve Bayes from scratch. After that, we will implement it using the scikit-learn package.

Implementing Naïve Bayes from scratch

Before we develop the model, let’s define the toy dataset we just worked with:

>>> import numpy as np
>>> X_train = np.array([
...     [0, 1, 1],
...     [0, 0, 1],
...     [0, 0, 0],
...     [1, 1, 0]])
>>> Y_train = ['Y', 'N', 'Y', 'Y']
>>> X_test = np.array([[1, 1, 0]])

For the model, starting with the prior, we first group the data by label and record their indices by classes:

>>> def get_label_indices(labels):
...     """
...     Group samples based on their labels and return indices
...     @param labels: list of labels
...     @return: dict, {class1: [indices], class2: [indices]}
...     ...

Evaluating classification performance

Beyond accuracy, there are several metrics we can use to gain more insight and avoid class imbalance effects. These are as follows:

Confusion matrix
Precision
Recall
F1 score
The area under the curve

A confusion matrix summarizes testing instances by their predicted values and true values, presented as a contingency table:

Figure 2.8: Contingency table for a confusion matrix

To illustrate this, we can compute the confusion matrix of our Naïve Bayes classifier. We use the confusion_matrix function from scikit-learn to compute it, but it is very easy to code it ourselves:

>>> from sklearn.metrics import confusion_matrix
>>> print(confusion_matrix(Y_test, prediction, labels=[0, 1]))
[[ 60  47]
 [148 431]]

As you can see from the resulting confusion matrix, there are 47 false positive cases (where the model misinterprets a dislike as a like for a movie), and 148...

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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.

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Python Machine Learning by example emphasis on best practices in machine learning, making it an invaluable asset for data scientists, machine learning engineers, and analysts alike. The book effectively bridges the gap between theoretical concepts and real-world applications, allowing readers to build a solid foundation while also tackling more complex challenges.The book chapters provide readers with insights into advanced NLP techniques that are increasingly relevant in today's data-driven world. Additionally, the exploration of multimodal computer vision models using PyTorch and Hugging Face opens doors to innovative applications, such as image search engines and image classification.The practical examples provided throughout the book, such as predicting stock prices and developing image classifiers with convolutional neural networks (CNNs), are particularly engaging and help solidify the concepts presented. Liu's clear explanations and structured approach make complex topics accessible, ensuring that readers can follow along and apply what they learn effectively.Moreover, the book covers a wide range of key modeling techniques, from recurrent neural networks (RNNs) and transformers to support vector machines (SVMs) and regularization methods. The focus on avoiding overfitting through feature selection and other strategies is crucial for anyone serious about producing robust machine learning models.

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It will covers from the most basic concepts

C. C Chin Oct 14, 2024

Need hands on ML newbie!!Also Python newbie too but got computer science degree!!Ready all 5* reviews, book perfect for Machine learning newbie and Python newbie and AWS MLS-C01 exam and entry level machine learning specalty exam and Sagemaker studio!!All new for me!!!Need examples to make practice exams answers to help for AWS mls-c01 machine learning specalty exam AWS Sagemaker studio too, since all new to me!!!Got book October 13, 2024!! And pdf too!!Reading now to do ML example!!Got Oliver beginner book, udemy classBook 3 months old pretty new, October 14,2024!!!Explain Oliver beginner book got 3 of those!!

Jacob Smith Sep 21, 2024

This book is an absolute gem for anyone looking to dive deep into the world of machine learning using Python! From the moment I opened it, I was impressed by the clear, concise explanations and the practical examples that make even the most complex topics easy to understand.The author does a fantastic job of breaking down key machine learning algorithms, explaining not just the "how" but the "why" behind each method. The inclusion of real-world datasets and hands-on exercises makes it easy to follow along and apply what you've learned immediately.

David Sep 12, 2024

Python Machine Learning by Example is a fantastic resource for anyone learning machine learning. The book takes a hands-on approach, covering key concepts like classification, regression, and neural networks through real-world examples. I appreciated how the author guides you through building algorithms from scratch before using tools like TensorFlow and scikit-learn. The clear explanations and coding exercises make it easy to follow, even for beginners. If you're looking to understand both the theory and implementation of machine learning, this book is a great choice!

Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases , Fourth Edition

What do you get with Print?

Python Machine Learning By Example

Building a Movie Recommendation Engine with Naïve Bayes

Getting started with classification

Exploring Naïve Bayes

Bayes’ theorem by example

Implementing Naïve Bayes

Implementing Naïve Bayes from scratch

Building a movie recommender with Naïve Bayes

Evaluating classification performance

Tuning models with cross-validation

Summary

Exercises

References

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