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Your Rails app is full of data that can (and should!) be turned into useful information with some simple machine learning technqiues. We'll look at basic techniques that are both immediately applicable and the foundation for more advanced analysis -- starting with your Users table. We will cover the basics of assigning users to categories, segmenting users by behavior, and simple recommendation algorithms. Come as a Rails dev, leave a data scientist. Help us caption & translate this video! http://amara.org/v/FGZx/
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In the workshop titled "Machine Learning For Fun and Profit" presented by John Paul Ahenfelter at RailsConf 2014, participants are introduced to basic machine learning techniques applicable to their Ruby on Rails applications. The main theme revolves around leveraging data from user tables to generate insights that can enhance business profitability. Key Points Discussed: - **Understanding Users**: The workshop begins with the importance of user data, where Ahenfelter engages the audience about their user tables and business goals, emphasizing the necessity to understand users to retrieve meaningful data insights. - **Machine Learning Techniques**: Participants learn several foundational machine learning techniques starting from categorizing users, segmenting behavior, and employing recommendation algorithms. Ahenfelter stresses the importance of using science and data analytics effectively to derive actionable business insights. - **Practical Implementation**: A significant focus of the workshop is on hands-on coding examples, utilizing the 'sex machine' gem to assign gender to users based on first names and analyzing user data without extensive surveys. This approach aims to achieve better accuracy than traditional survey methods. - **Geolocation**: The presenter also covers geolocation, explaining how to derive rough user locations from IP addresses using free geo-IP services, which helps in understanding user demographics and tailoring support strategies accordingly. - **Clustering Algorithms**: The workshop highlights the concept of user segmentation through clustering algorithms like K-means and hierarchical clustering, which aid in identifying different user groups based on their interaction with the application. Ahenfelter provides practical demonstrations, encouraging attendees to explore these algorithms for real-time data. - **Recommender Systems**: Finally, attendees are introduced to recommendations using Single Value Decomposition (SVD) to find similar users based on their interactions, which could assist in personalizing user experience and increasing engagement. Takeaways: - The workshop emphasizes the need for Rails developers to become data scientists to utilize the wealth of user data effectively. - Participants take home practical knowledge about applying machine learning in their own applications, equipped with the tools to answer user-related business questions and drive profitability.
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