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)
15
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16
Index

Finding the separating boundary with SVM

Now that you have been introduced to a powerful yet simple classifier, Naïve Bayes, we will continue with another great classifier, SVM, which is effective in cases with high-dimensional spaces or where the number of dimensions is greater than the number of samples.

In machine learning classification, SVM finds an optimal hyperplane that best segregates observations from different classes. A hyperplane is a plane of n - 1 dimensions that separates the n-dimensional feature space of the observations into two spaces. For example, the hyperplane in a two-dimensional feature space is a line, and in a three-dimensional feature space the hyperplane is a surface. The optimal hyperplane is picked so that the distance from its nearest points in each space to itself is maximized. And these nearest points are the so-called support vectors. The following toy example demonstrates what support vectors and a separating hyperplane (along with...