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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Understanding the dataset


In this section, we will look into our dataset. We have considered an IMDb dataset, which you can download at: http://ai.stanford.edu/~amaas/data/sentiment/. After clicking on this link, you can see that there is a link provided on the page. This link is titled Large Movie Review Dataset v1.0; we need to click on it. This way, we can download the IMDb dataset. Once you have downloaded the dataset, you need to extract the .tar.gz file. Once you extract the .tar.gz file, you can see that there are two folders inside the extracted folder and some other files. Let's look at each of them in the following section.

Understanding the content of the dataset

After extracting the dataset file, we'll see that there are some folders and files inside it. We will be discussing all of the content's meaning and what we will be using for our training purposes. This dataset has two folders and three files:

  • train folder

  • test folder

  • imdb.vocab file

  • imdbEr.txt

  • README

Train folder

This folder...

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