Название: Machine Learning for Tabular dаta: XGBoost, Deep Learning, and AI (Final Release)
Автор: Mark Ryan, Luca Massaron
Издательство: Manning Publications
Год: 2025
Страниц: 504
Язык: английский
Формат: epub (true)
Размер: 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.

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.

This book describes how you can use Machine Learning approaches, including classical approaches based on gradient boosting, Deep Learning, and Generative AI, to extract valuable insights from tabular data (structured data organized in rows and columns) that you can apply to your job. You will learn about the defining characteristics of tabular data, best practices for applying Machine Learning to tabular data, and how to take a model you have trained on tabular data and make it available for others to use. You will also learn the pros and cons of classical Machine Learning and Deep Learning when it comes to solving tabular data problems. Throughout the book, you will learn to expedite the analysis process by using Generative AI.

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.

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). Along the way, you’ll learn to make your models both more powerful and more explainable.

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

About the reader:
For readers experienced with Python and the basics of Machine Learning.

This book is intended for an audience that spans data scientists, AI/machine learning engineers, and business stakeholders. To get the most out of this book, you should have a basic grounding in classical machine learning techniques and exposure to deep learning. The code examples that appear throughout this book use Python, and most are designed to be run in hosted Jupyter Notebook environments such as Google Colab. You will get the most out of these code examples if you have already been exposed to standard Python methods of working with tabular data, such as the pandas and NumPy packages. In addition to the standalone examples designed to be run in Jupyter Notebooks, chapters 10 and 11 include examples that run in the Google Cloud environment. You don’t need to already know Google Cloud to enjoy these examples—we’ll tell you everything you need to know from the very start—but if you have already done some work in one of the major cloud environments (AWS, Azure, or Google Cloud), you will have a head start with the examples in these two chapters.