books & projects by Mark Ryan
Machine Learning for Tabular Data
Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable.
End-to-End Deep Learning for Price Prediction
In this practical series of liveProjects, you’ll learn how to bring the power of deep learning to your structured, tabular data. Go hands-on with an open dataset detailing Airbnb rentals in New York City, and take on the challenge of creating an end-to-end deep learning solution for predicting prices. Each project revolves around an essential task of the deep learning pipeline, and is a great way to get started applying deep learning to real-world problems. Work from beginning to end, or dive into whichever section will best augment your skills.
Deep Learning with Structured Data
Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring.
