Suggest modification to this talk

Title

Description

Five machine learning techniques that you can use in your Ruby apps today By Benjamin Curtis

Machine learning is everywhere these days. Features like search, voice recognition, recommendations - they've become so common that people have started to expect them. They're starting to expect the apps we build to be smarter.
Ten years ago, machine learning and data mining techniques were only available to the people dedicated enough to dig through the math. Now that's not the case.
The most common machine learning techniques are well known. Standard approaches have been developed. And, fortunately for us, many of these are available as ruby gems. Some are even easy to implement yourself.
In this presentation we'll cover five important machine learning techniques that can be used in a wide range of applications. It will be a wide and shallow introduction, for Rubyists, not mathematicians - we'll have plenty of simple code examples.
By the end of the presentation, you won't be an expert, but you'll know about a class of tools you may not have realized were available.

Help us caption & translate this video!

http://amara.org/v/FG2G/

Date

Summarized using AI?

If this talk's summary was generated by AI, please check this box. A "Summarized using AI" badge will be displayed in the summary tab to indicate that the summary was generated using AI.

Show "Summarized using AI" badge on summary page

Summary

Markdown supported

In the presentation titled "Five Machine Learning Techniques That You Can Use In Your Ruby Apps Today," Benjamin Curtis discusses the increasing relevance of machine learning (ML) techniques for Ruby application developers. The goal is to introduce a selection of practical ML strategies that can enhance the functionality of Ruby apps without requiring advanced mathematical knowledge. The presentation is structured around the following key points:

- **Introduction to Machine Learning**: Curtis emphasizes that machine learning is becoming increasingly integral to applications, helping developers leverage data to create smarter solutions.

- **Importance of Data Logging**: He highlights the necessity of having substantial data, noting that effective logging is foundational for any ML application. Various logging tools are suggested, such as Logstash, Elasticsearch, and Apache Kafka.

- **Clustering Techniques**: The concept of clustering is introduced with K-means as a primary example. Curtis explains how K-means organizes data points into clusters based on their similarities, aiding in data visualization and comprehension.

- **Supervised Learning with Decision Trees**: The presentation shifts to supervised learning techniques, using decision trees to demonstrate how conditions can classify data. An example includes using decision trees to automatically generate articles during significant news events.

- **Use of Classifiers in Sentiment Analysis**: Curtis discusses classifiers, particularly naive Bayesian classifiers, and their application in analyzing text for sentiment. This technique helps businesses refine customer engagement strategies based on user feedback.

- **Latent Semantic Indexing (LSI)**: LSI is presented as a method for understanding the relationship between words and documents, improving search capabilities and recommendations.

- **Recommendation Engines**: Curtis explains how recommendation algorithms, focusing on collaborative filtering, enhance user experiences by suggesting content based on previous behaviors, showcasing their utility in e-commerce.

- **Jaccard Index**: Another algorithm referenced is the Jaccard index, which analyzes the similarity between datasets, proving useful in clustering and improving recommendation systems.

- **Conclusion**: Curtis concludes by reinforcing the potential of machine learning techniques to enhance Ruby applications. He encourages developers to explore and integrate these methodologies to make their applications smarter and more efficient.

Overall, the presentation serves as an accessible introduction to various machine learning techniques that Ruby developers can adopt, emphasizing practical applications and encouraging further exploration.

Cancel