Machine Learning For Tabular Data Xgboost Deep Learning And Ai 1st Edition Mark Ryan by Mark Ryan, Luca Massaron instant download after payment.

Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques.

Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. 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. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day.

Foreword by Antonio Gulli.

Purchase of the print book includes a free eBook in PDF and EPUB formats from Manning Publications.

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,