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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Recognizing Faces with Support Vector Machine

In the previous chapter, we built a movie recommendation system with Naïve Bayes. This chapter continues our journey of supervised learning and classification. Specifically, we will be focusing on multiclass classification and support vector machine (SVM) classifiers. SVM is one of the most popular algorithms when it comes to high-dimensional spaces. The goal of the algorithm is to find a decision boundary in order to separate data from different classes. We will be discussing in detail how that works. Also, we will be implementing the algorithm with scikit-learn, and applying it to solve various real-life problems, including our main project of face recognition, along with fetal state categorization in cardiotocography and breast cancer prediction. A dimensionality reduction technique called principal component analysis, which boosts the performance of the image classifier, will also be covered in the chapter.