Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

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.
Table of Contents (17 chapters)
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At first glance, the features in the preceding dataset are categorical, for example, male or female, one of four age groups, one of the predefined site categories, or whether the user is interested in sports. Such data is different from the numerical feature data we have worked with until now.

Categorical (also called qualitative) features represent characteristics, distinct groups, and a countable number of options. Categorical features may or may not have a logical order. For example, household income from low to medium to high is an ordinal feature, while the category of an ad is not ordinal.

Numerical (also called quantitative) features, on the other hand, have mathematical meaning as a measurement and, of course, are ordered. For instance, term frequency and the tf-idf variant are discrete and continuous numerical features, respectively; the cardiotocography...