Sentiment Analysis through Deep Learning with Keras & Python
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 599 MB
Duration: 3 hours | Genre: eLearning | Language: English

Learn to apply sentiment analysis to your problems through a practical, real world use case

What you'll learn

Learn industry grade sentiment analysis with less than 60 lines of code
Understand how sentiment analysis with deep learning is easy and efficient
Take your understanding to the next level by extending the modular code developed in this course
Get great SUPPORT from an instructor with decades of experience
Use Python for sentiment analysis instead of some other less useful language

Requirements

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

Description

Do you want to learn to do sentiment analysis? The answer should almost always be yes if you are working in any business domain. Every company on the face of the earth wants to know what its customers feel about its products and services - and sentiment analysis is the easiest way and most accurate way of finding out the answer to this question.

By learning to do sentiment analysis, you would be making yourself invaluable to any company, especially those which are interested in quality assurance of their products and those working with business intelligence (which is almost all sensible companies, large and small, nowadays).

And in this course, we make doing sentiment analysis really easy. In the very first video, we 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. After you are done with this course, you would immediately be able to plug this system into your existing pipelines to do sentiment analysis of any text you can throw at it.

That is one of the reasons you should be doing sentiment analysis using Python and not some other "data science language" such as R. If you work with R and do sentiment analysis, you would still have to put in a lot of effort to take this skill to the market. If you write your sentiment analysis engine in Python, incorporating your code into your final business product is dead easy.

The second important tip for sentiment analysis is the latest success stories do not try to do it by hand. Instead, you train a machine to do it for you. That is why we use deep sentiment analysis in this course: you will train a deep learning model to do sentiment analysis for you. That way, you put in very little effort and get industry standard sentiment analysis - and you can improve your engine later on by simply utilizing a better model as soon as it becomes available with little effort.

We will focus on the following:

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

About the instructor:

Teacher and researcher by profession

PhD in Security and a PostDoc from Max Planck Institute for Software Systems, Germany

17+ years of working with computers and 15+ years of teaching experience

5+ years of working extensively with deep learning. I worked with almost all the modern tools as soon as they were released

Best seller instructor on Udemy with many highly rated courses!

Target Audience:

Anyone who:

wants to do sentiment analysis in the real world

wants to understand how deep learning can help with sentiment analysis

is working for a company that wants to see how its products are doing with their customers

What you need to know:

Python basics (installation, if, loops, lists) - Everything else will be covered in the course

No machine learning background is assumed (but we keep the theory to a minimum)

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



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