About This Course

Who this course is for:

  • Anyone who wants to do sentiment analysis in the real world
  • Anyone who wants to understand how deep learning can help with sentiment analysis
  • Anyone who is working for a company that wants to see how its products are doing with their customers

What you’ll learn: 

  • 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

Requirements: 

  • Basic understanding of the Python language
  • No deep learning or sentiment analysis background assumed

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. 

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

Section 1 - Introduction
Bird’s Eye View Of Deep Sentiment Analysis 00:00:00
MNIST Dataset Description 00:00:00
Learning And Prediction Pipeline 00:00:00
Section 2 - Bare Essentials Of Theory
Machine Learning Pipeline 00:00:00
Regression 00:00:00
Neural Networks – A Modular Approach 00:00:00
Recap And Supporting Talk 00:00:00
Section 3 - Getting Started With Keras
Windows Installation And Hurdles 00:00:00
Mac And Linux Installation 00:00:00
Keras : Data Preparation 00:00:00
Learning And Evaluation With Keras 00:00:00
Section 4 - Sentiment Analysis Case Study
Understanding The Sentiment Data 00:00:00
Structure Of Data For Deep Learning 00:00:00
Model, Embedding And Applying To Real World 00:00:00
Section 5 - Convolutional Neural Networks With Keras
Basics of Convolutional Neural Networks 00:00:00
ConvNet With Keras 00:00:00
Pooling And Translation Invariance 00:00:00
Dropout And Regularization 00:00:00
Using The Functional API With CNN 00:00:00
Section 6 - Revisiting The Sentiment Analysis Model
CNN, LSTM And Other Models For Sentiment Analysis 00:00:00
Section 7 - Finishing Up
Saving And Loading Model Weights 00:00:00
Parting Words And Future Directions 00:00:00
Downloadable Material 00:00:00
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