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Mobile Artificial Intelligence Projects

You're reading from  Mobile Artificial Intelligence Projects

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344073
Pages 312 pages
Edition 1st Edition
Languages
Authors (3):
Karthikeyan NG Karthikeyan NG
Profile icon Karthikeyan NG
Arun Padmanabhan Arun Padmanabhan
Profile icon Arun Padmanabhan
Matt Cole Matt Cole
Profile icon Matt Cole
View More author details

Table of Contents (12) Chapters

Preface 1. Artificial Intelligence Concepts and Fundamentals 2. Creating a Real-Estate Price Prediction Mobile App 3. Implementing Deep Net Architectures to Recognize Handwritten Digits 4. Building a Machine Vision Mobile App to Classify Flower Species 5. Building an ML Model to Predict Car Damage Using TensorFlow 6. PyTorch Experiments on NLP and RNN 7. TensorFlow on Mobile with Speech-to-Text with the WaveNet Model 8. Implementing GANs to Recognize Handwritten Digits 9. Sentiment Analysis over Text Using LinearSVC 10. What is Next? 11. Other Books You May Enjoy

Building the ML model using scikit–learn

In this section, we will build our own model. There are existing datasets available that are related to Twitter feed data on the topic of product and movie reviews. You can pick a dataset that suits you; in this chapter, we will pick a dataset that has customer reviews.

A dataset that contains both positive and negative reviews of customers can be found at http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW3/. You can download the dataset from the following link: http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW3/epinions3.zip.

The aforementioned dataset has both positive and negative feedback about a product, as shown in the following screenshot:

We will train the dataset using the scikit-learn pipeline and LinearSVC. Let's take a closer look at both of these.

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