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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In this chapter, we continued our journey of supervised learning with SVM. You learned about the mechanics of an SVM, kernel techniques and implementations of SVM, and other important concepts of machine learning classification, including multiclass classification strategies and grid search, as well as useful tips for using an SVM (for example, choosing between kernels and tuning parameters). Then, we finally put into practice what you learned in the form of real-world use cases, including face recognition and fetal state classification.

You have learned and adopted two classification algorithms so far, Naïve Bayes and SVM. Naïve Bayes is a simple algorithm (as its name implies). For a dataset with independent, or close to independent, features, Naïve Bayes will usually perform well. SVM is versatile and adaptive to the linear separability of data. In general, high accuracy can be achieved by SVM with the right kernel and parameters. However...