Sentiment Analysis and Brand Monitoring

Sentiment Analysis and Brand Monitoring

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Summary

Thanks to machine learning, anomaly detection has never been easier. This instructor-led course will show you how to leverage the machine learning capabilities of the Elastic Stack to find anomalous data.
Note: This course is a module of the Data Science specialization.

Description

Using Twitter data in a series of labs, you will learn to ingest and enrich data via an external API, and then analyze social network data with the Elastic Stack. Then, using machine learning, you will learn how to quickly detect anomalous behavior, such as spam tweets. After completing this course, you will be able to start using machine learning in your Elasticsearch clusters.

Topics Covered

  • Solving Problems with Machine Learning
  • Ingesting Twitter Data
  • Sentiment Analysis with Python and Logstash
  • Monitoring APIs with Kibana
  • Anomaly Detection with Machine Learning
Download Course Outline

Length

2-3 hours

Duration

3 hours

Audience

Data Scientists, Data Architects, Data Engineers

Prerequisites

Setup Requirements

  • Stable internet connection
  • Mac, Linux, or Windows
  • Latest version of Chrome or Firefox (Safari is not 100% supported)
  • Due to virtual classroom JavaScript requirements, we recommend that you disable any ad-blockers and restart your browser before class.

Additional Notes

Virtual Classroom Information
This instructor-led course is only taught in a virtual classroom environment. We encourage participants to set up their systems prior to logging into the training environment the first day of the class. We also encourage being logged into the virtual classroom 15 minutes prior to the start of class each day.

Training Specializations

This course is a module of the Data Science specialization. Find out how our focused Training Specializations can help you with your use case.

General Training Information
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