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)

Classifying newsgroups

In this recipe, we'll do a relatively simple supervised task: based on texts, we'll train a model to determine what an article is about, from a selection of topics. This is a relatively common task with NLP; we'll try to give an overview of different ways to approach this.

You might also want to compare the Battling algorithmic bias recipe in Chapter 2, Advanced Topics in Supervised Machine Learning, on how to approach this problem using a bag-of-words approach (CountVectorizer in scikit-learn). In this recipe, we'll be using approaches with word embeddings and deep learning models using word embeddings.

Getting ready

In this recipe, we'll be using scikit-learn and TensorFlow (Keras), as in so many other recipes of this book. Additionally, we'll use word embeddings that we'll have to download, and we'll use utility functions from the Gensim library to apply them in our machine learning pipeline:

!pip install gensim