Summary

This post is part of a series that covers Artificial Intelligence with a focus on Elastic's (Creators of Elasticsearch) Machine Learning solution, aiming to introduce and exemplify the possibilities and options available, in addition to addressing the context and usability.

Content

• 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
• NLP and Elastic: Getting started
• NLP HandsOn

HandsOn Setup - Elastic Cloud
Resources
Note: It is recommended to read the posts following the sequence of topics above.

Learn more!

• Other blogs:

Enhancing chatbot capabilities with NLP and vector search in Elasticsearch

Lexical and Semantic Search with Elasticsearch

NLP: Using a Question Answering model to talk to your favorite Christmas song

• Talks:

Extracting insights from unstructured data: Exploring Vector Search and NLP in Elasticsearch

Improving Retrieval Quality For Text Search