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

Fetal state classification on cardiotocography

We are going to build a classifier that helps obstetricians categorize cardiotocograms (CTGs) into one of the three fetal states (normal, suspect, and pathologic). The cardiotocography dataset we will use is from https://archive.ics.uci.edu/ml/datasets/Cardiotocography in the UCI Machine Learning Repository, and it can be directly downloaded from https://archive.ics.uci.edu/ml/machine-learning-databases/00193/CTG.xls as an .xls Excel file. The dataset consists of measurements of fetal heart rate and uterine contraction as features, and the fetal state class code (1=normal, 2=suspect, 3=pathologic) as a label. There are in total 2,126 samples with 23 features. Based on the numbers of instances and features (2,126 is not significantly larger than 23), the RBF kernel is the first choice.

We will work with the Excel file using pandas, which is suitable for table data. It might request an additional installation...