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)

Recommending products

In this recipe, we'll be building a recommendation system. A recommender is an information-filtering system that predicts rankings or similarities by bringing content and social connections together.

We'll download a dataset of book ratings that have been collected from the Goodreads website, where users rank and review books that they've read. We'll build different recommender models, and we'll suggest new books based on known ratings.

Getting ready

To prepare for our recipe, we'll download the dataset and install the required dependencies.

Let's get the dataset and install the two libraries we'll use here – spotlight and lightfm are recommender libraries:

!pip install git+ lightfm

Then we need to get the dataset of book ratings:

from spotlight.datasets.goodbooks import get_goodbooks_dataset
from spotlight.cross_validation import random_train_test_split

import numpy as np