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