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Sentiment Analysis in Python

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Course Report - Sentiment Analysis in Python

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Are you a person who leaves reviews to let others know what you think about a product? Are you able to read product reviews online before purchasing a product? These reviews are valuable not only for you but also for businesses. This course will help you interpret different documents' sentiments. You will use real-world datasets such as product reviews, tweets, movie trailers, and twitter. Additionally, you will use Python nltk and scikit-learn as well as other Python tools. This course will prepare you to do an end-to-end sentiment analysis task, based on the tweets of US airline passengers.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Python Data Science Toolbox (Part 2)

What You Will Learn

In this course, you will learn how to make sense of the sentiment expressed in various documents

You will use real-world datasets featuring tweets, movie and product reviews, and use Python’s nltk and scikit-learn packages

By the end of the course, you will be able to carry an end-to-end sentiment analysis task based on how US airline passengers expressed their feelings on Twitter

Course Instructors

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Violeta Misheva

Data Scientist

Violeta is a data scientist passionate about machine learning, natural language processing and fair and explainable algorithms, among others. She supplements her machine learning knowledge with her d...

Course Reviews

Average Rating Based on 3 reviews

5.0

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