Sentiment Analysis and Brand Monitoring
Thanks to machine learning, anomaly detection has never been easier. This 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.
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.
- Solving Problems with Machine Learning
- Ingesting Twitter Data
- Sentiment Analysis with Python and Logstash
- Monitoring APIs with Kibana
- Anomaly Detection with Machine Learning
Data Scientists, Data Architects, Data Engineers
- We recommend you have taken Elasticsearch Engineer I and Elasticsearch Engineer II or possess equivalent knowledge. Engineer I and Engineer II teach the concepts that are the foundation upon which all specializations are built.
- Familiarity with Logstash
- A Twitter developer account
- Stable internet connection
- Mac, Linux, or Windows
- Latest version of Chrome or Firefox (Safari is not 100% supported)
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