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

Learning without guidance – unsupervised learning

In the previous chapter, we applied t-SNE to visualize the newsgroup text data, reduced to 2 dimensions. T-SNE, or dimensionality reduction in general, is a type of unsupervised learning. Instead of having a teacher educating on what particular output to produce, such as a class or membership (classification), and a continuous value (regression), unsupervised learning identifies inherent structures or commonalities in the input data. Since there is no guidance from the "teacher" in unsupervised learning, there is no clear answer on what is a right or wrong result. Unsupervised learning has the freedom to discover hidden information underneath input data.

An easy way to understand unsupervised learning is to think of going through many practice questions for an exam. In supervised learning, you are given answers to those practice questions. You basically figure out the relationship between the questions and...