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

Yuxi (Hayden) Liu

$45.99

4.9 (8 Ratings)

Paperback Jul 2024 518 pages 4th Edition

Yuxi (Hayden) Liu

$45.99

4.9 (8 Ratings)

Paperback Jul 2024 518 pages 4th Edition

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

View table of contents

Preview Book

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...

Download Code

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
Purchase of the print or Kindle book includes a free PDF copy

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

Estimated delivery fee Deliver to United States

Economy delivery 10 - 13 business days

Free ~~$6.95~~

Premium delivery 6 - 9 business days

$21.95

(Includes tracking information)

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

$45.99

$51.99

$52.99

Total $ 150.97

FAQs

What is the delivery time and cost of print book?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.

Unfortunately, due to several restrictions, we are unable to ship to the following countries:

Afghanistan
American Samoa
Belarus
Brunei Darussalam
Central African Republic
The Democratic Republic of Congo
Eritrea
Guinea-bissau
Iran
Lebanon
Libiya Arab Jamahriya
Somalia
Sudan
Russian Federation
Syrian Arab Republic
Ukraine
Venezuela

What is custom duty/charge?

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order?

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges?

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.

How can I cancel my order?

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy?

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged?

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use?

You can pay with the following card types:

Visa Debit
Visa Credit
MasterCard
PayPal

What is the delivery time and cost of print books?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Unfortunately, due to several restrictions, we are unable to ship to the following countries:

Afghanistan
American Samoa
Belarus
Brunei Darussalam
Central African Republic
The Democratic Republic of Congo
Eritrea
Guinea-bissau
Iran
Lebanon
Libiya Arab Jamahriya
Somalia
Sudan
Russian Federation
Syrian Arab Republic
Ukraine
Venezuela

Filter reviews by

All

Amazon verified reviews

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.

Amazon Verified review

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

Join our book’s Discord space

Page 1 of 11

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs

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

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs