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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No machine learning system can be built without data. Therefore, data collection should be our first focus.

Best practice 1 – Completely understanding the project goal

Before starting to collect data, we should make sure that the goal of the project and the business problem is completely understood, as this will guide us on what data sources to look into, and where sufficient domain knowledge and expertise is also required. For example, in a previous chapter, Chapter 7, Predicting Stock Prices with Regression Algorithms, our goal was to predict the future prices of the DJIA index, so we first collected data of its past performance, instead of the past performance of an irrelevant European stock. In Chapter 4, Predicting Online Ad Click-Through with Tree-Based Algorithms, for example, the business problem was to optimize advertising targeting efficiency measured by click-through rate, so we collected...