Priscilla ParodiSummary
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
