Python Machine Learning by Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn , Third Edition

Yuxi (Hayden) Liu

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4 (20 Ratings)

Paperback Oct 2020 526 pages 3rd Edition

Yuxi (Hayden) Liu

€29.99

4 (20 Ratings)

Paperback Oct 2020 526 pages 3rd 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. It is a good starting point to learn classification from a real-life example—movie streaming service providers are already doing this, and we can do the same. 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:

What is machine learning classification?
Types of classification
Applications...

Implementing Naïve Bayes

After calculating by hand the movie preference prediction example, as promised, we are going to code 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 to avoid class imbalance effects. These are as follows:

Confusion matrix
Precision
Recall
F1 score
Area under the curve

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

Table 2.3: 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...

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Key benefits

Dive into machine learning algorithms to solve the complex challenges faced by data scientists today
Explore cutting edge content reflecting deep learning and reinforcement learning developments
Use updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-end

Description

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

Who is this book for?

If you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.

What you will learn

Understand the important concepts in ML and data science
Use Python to explore the world of data mining and analytics
Scale up model training using varied data complexities with Apache Spark
Delve deep into text analysis and NLP using Python libraries such NLTK and Gensim
Select and build an ML model and evaluate and optimize its performance
Implement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learn

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Amazon verified reviews

siebo Dec 15, 2020

Excellent book, I really enjoy Yuxi's writing style. He is equally adept at explaining algos, classifiers, and code architecture as he is navigating the business cases, design workflows and the stories behind the data. Essentially, everything you would expect in a top-flight Google engineer. If you work in AdTech or Analytics-driven Marketing, the book is an obvious buy for chapters 4-6 alone on predicting and optimizing ad click-through. Yuxi manages to fit a huge amount of content into this book, while delivering each topic in concise, approachable writing. At 500+ pages, he is not cutting any corners. My favorite chapters were the ones on Facial Recognition and predicting financial market pattern, as those are the most relevant to my own work. The Best Practices section in Ch. 11 will save you from a lot of issues. I think this book would be approachable to beginners, as the author starts from a foundation level and goes up from there. For advanced who want to dive deeper, Python Machine Learning (from the same publisher) is a good follow-on, and covers some of the topics from this book in greater detail.

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Annie Jun 19, 2021

The concepts are explained clearly and step by step. Machine learning is not an easy topic, I find that this book is really helpful

Brandon Dec 01, 2020

The book is a great practical resource for those interested in applying ML techniques quickly. As other reviewers have mentioned, it is a bit light on theory and the more technical aspects of ML until you get deeper into the book. Having said that, the examples and code provided are very practical and to the point. I would recommend it if you are either already familiar with basic ML theory and the math behind it or if you have a software engineering background and are simply looking to quickly implement an ML solution. The author walks you through code segments and explains each step and by the end, you will have covered most of the more popular ML models and know which ones to use given your data and your goals.The chapters on SVMs and building and predicting ad-clicks are very practical. I like how the author walks through each model type and compares them so you have a baseline and can see the differences in implementation and result. That was a helpful exercise spread across a few chapters. Also, the Best Practices chapter near the end was a good, "I need an answer quickly" kind of reference.Overall, I liked the book and think it would be helpful from a more practical perspective. I think this book paired with another more theoretical resource would really round out those seeking to learn ML.

crystalattice Nov 23, 2020

Python ML By Example (BE) is a good complement to Python ML Third Edition (3E). The 3E book focuses on the theory and general application of ML programming, while the BE book focuses an specific application examples.While they both tackle ML programming, their approach is different. The BE book assumes you have a reasonable, foundational background in ML and uses that basis to create specific ML-based applications.For example, whereas 3E has a simple note about Naïve Bayes classification, the BE book has a whole chapter dedicated to the algorithm, discussing the different types of classification methods, how Naïve Bayes works, and then actually implementing a Naïve Bayes application. On the flip side, the 3E book has a whole chapter dedicated just to the different classifiers and different implementations of them using scikit-learn.It's almost like the 3E book is a textbook and the BE book is its complementary workbook for practice. While you may be able to be successful with either one, combining them really maximizes your ML learning.To speak about the BE book in more detail, the topics covered include:*Introduction to Python ML, including software installation*Using Naïve Bayes algorithm to create movie recommendation application*Using SVM for facial recognition*Using tree-based algorithms to predict ad click-through*Using Apache Spark to work with large data sets*Using regression algorithms and neural networks to predict the stock market*Using text analysis and NLP to data mine newsgroups*Using unsupervised learning models to identify newsgroups topics*Using different types of neural networks for different types of analysis approaches*Using reinforcement learning for decision making*ML best practicesIt is a long book (nearly 500 pages), but the material is invaluable for anyone in the ML field, especially if you don't have a lot of experience with the different algorithms. And in conjunction with 3E, you almost have a complete ML curriculum.

Matt M Dec 08, 2020

It is a fantastic, well-written book in machine learning with python. It provides enough technical background about each technique's theory, followed by beautiful graphs/tables and its Python code.It covers various topics and practical examples, such as the powerful and popular classification models in supervised learning, regression algorithms, reinforcement learning, and much more.This book prepares you for real-world machine learning problems. I highly recommended it!

Python Machine Learning by Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn , Third 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

Learning Bayes' theorem...

Implementing Naïve Bayes

Implementing Naïve Bayes from scratch

Building a movie recommender with Naïve Bayes

Evaluating classification performance

Tuning models with cross-validation

Summary

Exercise

References

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