This post is part of a series that covers Artificial Intelligence with a focus on Elastic's (Creators of Elasticsearch, Kibana, Logstash and Beats) Machine Learning solution, aiming to introduce and exemplify the possibilities and options available, in addition to addressing the context and usability.
- Introduction to Artificial Intelligence and Data Analytics
- Machine Learning - Types of Learning
- Elastic Anomaly Detection - Learning Process and Anomaly Score
- Elastic Anomaly Detection - Categorization
- Elastic Anomaly Detection and Data Visualizer HandsOn
- Elastic Data Frame - Outlier Detection
- Elastic Data Frame - Regression Analysis
- Elastic Data Frame - Classification Analysis
- Elastic Data Frame - Classification vs Regression
- Data preparation for Data Frame Analysis with Transforms
- Trained Models for Supervised Learning
- Inference for Supervised Learning
- Elastic Data Frame - Classification Analysis HandsOn
- Elastic Data Frame - Inference Processor HandsOn
Note: It is recommended to read the posts following the sequence of topics above.