Keras, Python And Deep Learning For Sentiment Analysis

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This course is designed for those interested to learn the basics of sentiment analysis using Python, how to write your sentiment analysis engine, and how to incorporate the code into your final business product

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About This CourseBeginner

Learning to do sentiment analysis would make yourself invaluable to any company, especially those which are interested in quality assurance of their products and those working with business intelligence.

In this course, you will learn how to plug a system into your existing pipelines to do sentiment analysis of any text you can throw at it. In the beginning, we will introduce a less than 60 line sentiment analysis engine that can perform industry grade sentiment analysis. We then spend the rest of the course explaining these very powerful 60 lines so that you have a thorough understanding of the code. 

What this course covers:

  • Understanding how to write industry grade sentiment analysis engines with very little effort
  • Basics of machine learning with minimal math
  • Understand not only the theoretical and academic aspects of sentiment analysis but also how to use it in your own field — real world sentiment analysis
  • Tips on avoiding mistakes made by new-comers to the field and the best practices to get you to your goal with minimal effort

Our Promise to You

By the end of this course, you will have learned sentiment analysis using Python and Deep Learning.

 30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

 Get started today and learn more about sentiment analysis using Python.

 

Course Curriculum

Course Sections

Bird’s Eye View Of Deep Sentiment Analysis

MNIST Dataset Description

Learning And Prediction Pipeline

Machine Learning Pipeline

Regression

Neural Networks – A Modular Approach

Recap And Supporting Talk

Windows Installation And Hurdles

Mac And Linux Installation

Keras : Data Preparation

Learning And Evaluation With Keras

Understanding The Sentiment Data

Structure Of Data For Deep Learning

Model, Embedding And Applying To Real World

Basics of Convolutional Neural Networks

ConvNet With Keras

Pooling And Translation Invariance

Dropout And Regularization

Using The Functional API With CNN

CNN, LSTM And Other Models For Sentiment Analysis

Saving And Loading Model Weights

Parting Words And Future Directions

Downloadable Material

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