Machine Learning for Tabular Data
XGBoost, Deep Learning, and AI
Mark / Massaron Ryan
504 pages, parution le 01/07/2025
Résumé
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:
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.
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.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Mannings publ. |
| Auteur(s) | Mark / Massaron Ryan |
| Parution | 01/07/2025 |
| Nb. de pages | 504 |
| Couverture | Broché |
| EAN13 | 9781633438545 |
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