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

Introducing reinforcement learning with examples

In this chapter, I will first introduce the elements of reinforcement learning along with an interesting example, then will move on to how we measure feedback from the environment, and follow with the fundamental approaches to solve reinforcement learning problems.

Elements of reinforcement learning

You may have played Super Mario (or Sonic) when you were young. During the video game, you control Mario to collect coins and avoid obstacles at the same time. The game ends if Mario hits an obstacle or falls in a gap. And you try to get as many coins as possible before the game ends.

Reinforcement learning is very similar to the Super Mario game. Reinforcement learning is about learning what to do. It observes the situations in the environment and determines the right actions in order to maximize a numerical reward. Here is the list of elements in a reinforcement learning task (I also link each element to Super Mario and other...