Keras, Python And Deep Learning For Sentiment Analysis

Skill Success

Certificate of completion

Digital certificate - Included

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

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.

Keras, Python And Deep Learning For Sentiment Analysis will cover the following topics:

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
• Regression
• 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
• Downloadable Material

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

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

None

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