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

Yuxi (Hayden) Liu

$34.49 ~~$45.99~~

4.9 (8 Ratings)

Paperback Jul 2024 526 pages 4th Edition

Yuxi (Hayden) Liu

$34.49 ~~$45.99~~

4.9 (8 Ratings)

Paperback Jul 2024 526 pages 4th Edition

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Python Machine Learning By Example

Building a Movie Recommendation Engine with Naïve Bayes

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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Key benefits

Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling
Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions
Implement ML models, such as neural networks and linear and logistic regression, from scratch

Description

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.

Who is this book for?

This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

What you will learn

Follow machine learning best practices throughout data preparation and model development
Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning
Develop and fine-tune neural networks using TensorFlow and PyTorch
Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP
Build classifiers using support vector machines (SVMs) and boost performance with PCA
Avoid overfitting using regularization, feature selection, and more

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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.

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Ayon Roy Sep 05, 2024

Starting my journey in machine learning was both exciting and overwhelming. I struggled to bridge the gap between theory and practical application in real-world projects. That’s why Yuxi Hayden Liu’s "Python Machine Learning by Example" has been a game-changer for me. This book offers a structured approach, making it easier to transition from learning to execution.Liu covers essential topics like overfitting, underfitting, and cross-validation right from the start, ensuring that you grasp the fundamentals. What truly sets this book apart is the hands-on projects that accompany each concept. From building a movie recommendation engine using Naive Bayes to predicting stock prices and exploring deep learning through artificial neural networks, Liu walks you through each step—from data preparation to model evaluation.The book is rich with best practices, such as feature engineering, algorithm selection, and monitoring model performance. By the end, you'll not only have a solid understanding of basic and advanced topics, including CNNs, transformer models, and reinforcement learning, but you’ll also feel confident applying them in real-world scenarios.Yuxi Hayden Liu’s industry experience shines through, making this book an invaluable guide for anyone feeling lost in their machine learning journey. Highly recommended for both students and professionals looking to elevate their skills. Happy reading!

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!!

saandeep sreerambatla Jul 31, 2024

"Python Machine Learning by Example, Fourth Edition" by Yuxi (Hayden) Liu is a fantastic resource for anyone interested in machine learning, whether you're just starting out or already have some experience. This book strikes a great balance between explaining the theory behind machine learning and showing you how to apply it in real-world scenarios, making it an essential addition to any data scientist’s collection.The book is well-organized, kicking off with the basics of machine learning and Python programming. Liu does an excellent job of explaining why machine learning is so important today and then helps you set up your Python environment. This ensures that even those with minimal programming experience can keep up.What really stands out about this book is its hands-on approach. Each chapter is packed with real-world examples that help bring complex machine learning concepts to life. For instance, the chapters on building a movie recommendation engine with Naïve Bayes and predicting stock prices with regression algorithms are particularly insightful, showing you exactly how these models work and how to apply them to real problems.The book also covers advanced topics like deep learning, natural language processing (NLP), and reinforcement learning. The sections on convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) for sequence prediction are especially useful. They provide a deep dive into these advanced models, complete with code examples using TensorFlow and PyTorch, which are incredibly helpful for anyone looking to implement these techniques in their own projects.Another great feature of this book is the focus on best practices. Liu includes 21 best practices that cover the entire machine learning workflow, from data preparation to model deployment and monitoring. This is invaluable for anyone looking to build robust and scalable machine learning solutions.It's worth noting that the book assumes you have a basic understanding of Python and some familiarity with statistical concepts. This might be a bit challenging for complete beginners, but it doesn't take away from the overall value of the book. Instead, it sets a realistic expectation for the level of expertise needed to fully benefit from the content.In conclusion, "Python Machine Learning by Example, Fourth Edition" is an excellent resource that bridges the gap between theory and practice. Yuxi (Hayden) Liu's clear explanations, practical examples, and focus on best practices make this book a must-read for anyone serious about mastering machine learning with Python. Whether you're a data analyst, a machine learning engineer, or a data scientist, this book will provide you with the tools and knowledge you need to succeed.

Thomas M. Aug 21, 2024

I highly recommend Liu's Python ML by Example! As a long term practitioner of all things analytics and data science, it was refreshing to come back to the foundations with this book. I wish I had this resource available when I was originally getting started in the field, as Liu has a knack for covering a broad range of salient topics in ML, while still offering plenty of depth for those looking to go into the weeds of how algorithms work. Super practical, this book focuses on real-life examples, spanning marketing & ads, content recommendations, text sentiment, image classification and beyond. The book also navigates tabular ML and deep learning concepts flawlessly. Liu doesn't stop at the fundamentals; the book also covers advanced topics like deep learning, natural language processing (NLP), and reinforcement learning. The sections on convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) for sequence prediction offer valuable insights into these cutting-edge techniques. These topics area all presented in ways that even new-to-ML readers would be able to grasp. These days, no ML book is complete without including GenAI as a topic, which the author integrates seamlessly. All around a super well rounded and practical read!

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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