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)

Spotting fraudster communities

In this recipe, we'll try to detect fraud communities using methods from network analysis. This is a use case that often seems to come up in graph analyses and intuitively appeals because, when carrying out fraud detection, we are interested in relationships between people, such as whether they live close together, are connected over social media, or have the same job.

Getting ready

In order to get everything in place for the recipe, we'll install the required libraries and we'll download a dataset.

We will use the following libraries:

Furthermore, we'll use SciPy, but this comes with the Anaconda distribution:

!pip install networkx annoy tqdm python-louvain

We'll use the following dataset...