Five machine learning techniques that you can use in your Ruby apps today By Benjamin Curtis Machine learning is everywhere these days
summary
Five concrete techniques with Ruby code and real tools not fluff: k‑means clustering (client marker‑cluster or server C for scale) to group map points, rule‑like decision trees for simple classification, naive Bayes and LSI for text classification and similarity, and recommender pipelines using the recommendable gem backed by Redis or large‑scale processing with Apache Mahout and Solr. Examples link to GitHub, a Wikipedia k‑means demo, and notes on performance tradeoffs.
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chapters (22)
- 0:00Introduction
- 2:54Making sense of data
- 3:24TCP and UDP
- 3:45Logging
- 5:10Tools for logging
- 5:50Clustering
- 6:27Kmeans clustering
- 7:51Kmeans clustering demo
- 9:17Map example
- 9:35Cluster
- 9:45MarkerCluster
- 10:24Kmeans
- 12:02Decision Trees
- 12:52Example
- 14:44Classification
- 15:09Bayesian Classification
- 16:32Classification Example
- 16:58Latent Semantic Analysis
- 20:09Jacquard Index
- 20:45Given a few arrays
- 22:16Recommendable
- 23:49Practical Machine Learning
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