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Forecasting the Future: An Introduction to Machine Learning for Weather Prediction in Native Ruby by Landon Gray Have you ever considered building a machine learning model in Ruby? It may surprise you to learn that you can train, build, and deploy ML models in Ruby. But what are the benefits of using Ruby over other languages? One of the biggest advantages of using Ruby for machine learning is accessibility. You're no longer limited to specific languages or tools when exploring AI and ML concepts. It's time for Rubyists to dive into the world of machine learning! - In this talk, we'll build a model that predicts the weather and explore the tools and libraries available for your own native Ruby Machine Learning projects. Whether you're a seasoned Rubyist or just starting out, don't miss this opportunity to discover the possibilities of machine learning in Ruby.
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In this video titled "Forecasting the Future: An Introduction to Machine Learning for Weather Prediction," speaker Landon Gray presents a compelling case for utilizing Ruby in the machine learning space, particularly in weather prediction applications. This talk is tailored for both seasoned Ruby developers and those new to the language, aiming to demystify the process of creating machine learning models in Ruby instead of the more commonly used Python. Key points discussed in the presentation include: - **Introduction to Machine Learning in Ruby**: Landon emphasizes the accessibility of machine learning in Ruby, encouraging developers who favor Ruby over Python to explore AI and ML concepts in their native language. - **Project Overview**: He outlines a project where the goal is to predict the maximum temperature using historical weather data from the Atlanta airport dating back to 1960. - **Data Collection and Preparation**: - The dataset is collected from the National Centers for Environmental Information, consisting of approximately 20,000 rows and 48 columns of data. - Data cleaning is emphasized, where significant time is spent preparing the dataset, typically 80% of the overall project time. - Landon discusses handling missing values, outliers, and duplicates to ensure the data is ready for analysis and model training. - **Machine Learning Libraries in Ruby**: He introduces three key libraries: - **Numo**: A numerical array class for fast data processing. - **Daru**: Provides data frame structures for analysis and visualization akin to Python's Pandas. - **Rumale**: A gem that facilitates various machine learning algorithms. - **Model Training and Prediction**: Landon explains the implementation of a linear regression model, highlighting how it attempts to model the relationship between variables by fitting a linear equation. - He provides clear, simple coding examples for training the model and making predictions based on the prepared data. - **Integration with Rails**: The talk concludes with insights on how to integrate the trained ML model into a Rails application for real-time predictions. Landon encourages developers to adopt machine learning in Ruby and to iterate on his presented project using their data. In conclusion, Landon Gray reiterates that creating machine learning models in Ruby is not only feasible but also accessible to all developers interested in exploring AI applications, reinforcing the idea that extensive academic experience is not a prerequisite for engaging with machine learning. To support these endeavors, he shares that the project will be available on GitHub for attendees to download, adapt, and utilize. This talk serves as an introduction and encouragement for Rubyists to venture into machine learning, fostering a community of learners and practitioners.
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