Book Image

Python Artificial Intelligence Projects for Beginners

By : Dr. Joshua Eckroth
Book Image

Python Artificial Intelligence Projects for Beginners

By: Dr. Joshua Eckroth

Overview of this book

Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress
Table of Contents (11 chapters)

Predicting bird species with random forests


Here we will be using random forests to predict a bird's species. We will use the Caltech-UC San Diego dataset (http://www.vision.caltech.edu/visipedia/CUB-200-2011.html), which contains about 12,000 photos of birds from 200 different species. Here we are not going to look at the pictures because that would need a convolutional neural network (CNN) and this will be covered in later chapters. CNNs can handle pictures much better than a random forest. Instead, we will be using attributes of the birds such as size, shape, and color.

Here are just some of the species in the dataset:

Some, such as the American Crow and the Fish Crow, are almost indistinguishable, at least visually. The attributes for each photo, such as color and size, have actually been labeled by humans. Caltech and UCSD used human workers on Amazon's Mechanical Turk to label the dataset. Researchers often use Mechanical Turk, which is a website service in which a person gets paid a...