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)

Detecting positive or negative sentiments in user reviews


In this section, we're going to look at detecting positive and negative sentiments in user reviews. In other words, we are going to detect whether the user is typing a positive comment or a negative comment about the product or service. We're going to use Word2Vec and Doc2Vec specifically and the gensim Python library for those services. There are two categories, which are positive and negative, and we have over 3,000 different reviews to look at. These come from Yelp, IMDb, and Amazon. Let's begin the code by importing the gensim library, which provides Word2Vec and Doc2Vec for logging to note status of the messages:

First, we will see how to load a pre-built Word2Vec model, provided by Google, that has been trained on billions of pages of text and has ultimately produced 300-dimensional vectors for all the different words. Once the model is loaded, we will look at the vector for cat. This shows that the model is a 300-dimensional...