Book Image

Artificial Intelligence with Python Cookbook

By : Ben Auffarth
Book Image

Artificial Intelligence with Python Cookbook

By: Ben Auffarth

Overview of this book

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.
Table of Contents (13 chapters)

Predicting stock prices with confidence

The efficient market hypothesis postulates that at any given time, stock prices integrate all information about a stock, and therefore, the market cannot be consistently outperformed with superior strategy or, more generally, better information. However, it can be argued that current practice in investment banking, where machine learning and statistics are built into algorithmic trading systems, contradicts this. But these algorithms can fail, as seen in the 2010 flash crash or when systemic risks are underestimated, as discussed by Roger Lowenstein in his book When Genius Failed: The Rise and Fall of Long-Term Capital Management.

In this recipe, we'll build a simple stock prediction pipeline in scikit-learn, and we'll produce probability estimates using different methods. We'll then evaluate our different approaches.

Getting ready

We'll retrieve historical stock prices using the yfinance library.

Here's how we install it...