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)
Other Books You May Enjoy

Exploring a decision tree from the root to the leaves

A decision tree is a tree-like graph, that is, a sequential diagram illustrating all of the possible decision alternatives and their corresponding outcomes. Starting from the root of a tree, every internal node represents the basis on which a decision is made. Each branch of a node represents how a choice may lead to the next nodes. And, finally, each terminal node, the leaf, represents the outcome produced.

For example, we have just made a couple of decisions that brought us to the point of using a decision tree to solve our advertising problem:

Figure 4.2: Using a decision tree to find the right algorithm

The first condition, or the root, is whether the feature type is numerical or categorical. Ad clickstream data contains mostly categorical features, so it goes to the right branch. In the next node, our work needs to be interpretable by non-technical clients. So, it goes...