English | 2025 | ISBN: 1633438546 | 504 pages| PDF | 37.2 MB
Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques.
You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline.
Machine Learning for Tabular Data will teach you how to
Pick the right machine learning approach for your data
Apply deep learning to tabular data
Deploy tabular machine learning locally and in the cloud
Pipelines to automatically train and maintain a model
Machine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches.
About the Technology
Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases and other tabular data sources using gradient boosting, deep learning, and generative AI.
About the Book
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).
What’s Inside
Master XGBoost
Apply deep learning to tabular data
Deploy models locally and in the cloud
Build pipelines to train and maintain models
