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

What is regression?

Regression is one of the main types of supervised learning in machine learning. In regression, the training set contains observations (also called features) and their associated continuous target values. The process of regression has two phases:

  • The first phase is exploring the relationships between the observations and the targets. This is the training phase.
  • The second phase is using the patterns from the first phase to generate the target for a future observation. This is the prediction phase.

The overall process is depicted in the following diagram:

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Figure 7.1: Training and prediction phase in regression

The major difference between regression and classification is that the output values in regression are continuous, while in classification they are discrete. This leads to different application areas for these two supervised learning methods. Classification is basically used to determine desired memberships or characteristics...