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

Solving the FrozenLake environment with dynamic programming

We will focus on the policy-based and value-based dynamic programming algorithms in this section. But let's start with simulating the FrozenLake environment.

Simulating the FrozenLake environment

FrozenLake is a typical OpenAI Gym environment with discrete states. It is about moving the agent from the starting tile to the destination tile in a grid, and at the same time avoiding traps. The grid is either 4 * 4 (https://gym.openai.com/envs/FrozenLake-v0/), or 8 * 8 (https://gym.openai.com/envs/FrozenLake8x8-v0/). There are four types of tiles in the grid:

  • S: The starting tile. This is state 0, and it comes with 0 reward.
  • G: The goal tile. It is state 15 in the 4 * 4 grid. It gives +1 reward and terminates an episode.
  • F: The frozen tile. In the 4 * 4 grid, states 1, 2, 3, 4, 6, 8, 9, 10, 13, and 14 are walkable tiles. It gives 0 reward.
  • H: The hole tile. In the 4 * 4 grid, states 5...