Exploring Machine Learning in Elixir with a Recommendation Engine

Andrew Forward, an enthusiast of Elixir programming and machine learning (ML), tackles the challenge of building a recommendation engine in Elixir rather than the typical Python approach. He explains how to leverage algorithms like KNearestNeighbour, Naive Bayes, and KMeans within the Elixir ecosystem through a library called 'scholar'. Andrew emphasizes the evolution of ML in Elixir and how his lack of production ML experience did not hinder exploring this space, thanks to his familiarity with Elixir. He conveys the importance of understanding ML fundamentals and integrates some ML into a Phoenix web application. Moreover, he provides an ML introduction by contrasting traditional programming with machine learning, highlighting the fitting and prediction process using Elixir's features. Andrew also advises on handling underfitting and overfitting in ML models, deciding between collaborative and content filtering for recommender systems, and the nuances of Elixir's utility for web development and structured data management.