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Predicting movie review sentiments with CNN & word embedding

Predicting movie review sentiments with CNN & word embedding
Link : new udemy course coupon code Predicting movie review sentiments with CNN & word embedding

How to Develop a Word Embedding Model for Predicting Movie ... that can predict the sentiment of a textual movie review as either positive or negative. ... We use a Convolutional Neural Network (CNN) as they have proven to ...NEW
by Evergreen Technologies

What you'll learn
Analyze sentiments of movie reviews using CNN, Glove embeddings and Keras

Description
Course Description

Learn to build sentiment analysis engine with CNN, keras, Glove and  popular programming language Python.

Build a strong foundation in sentiment analysis with this tutorial for beginners.

  • Understanding of sentiment classification
  • Understand the world of world embeddings
  • Learn CNN from scratch
  • Leverage CNN, Keras, Glove to classify sentiments of movie reviews
  • Learn how to represent text as numeric vectors using glove embeddings
  • Learn how to evaluate model from scratch
  • User Jupyter Notebook for programming
  • Build a real life web application to classify news
  • A Powerful Skill at Your Fingertips  Learning the fundamentals of text classification puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation.

No prior knowledge of deep learning or Machine learning is assumed. I a, covering topics like CNN, Word Embeddings Precision, Recall in depth so that  even beginners can understand this course very well.

Jobs in text classification systems area are plentiful, and being able to learn it with CNN and Keras will give you a strong edge.

Text Classification ares becoming very popular. Washington Post, News York Times, Amazon,  Google eCommerce websites are few famous example of text classification systems in action.  Text classification systems are vital in information retrieval and personalizing news.  Learning CNN with Keras will help you become a natural language processing (NLP) developer which is in high demand.

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