Managing Datasets and Models

Oswald Campesato

ISBN 10: 1683929527 / ISBN 13: 9781683929529

Published by De Gruyter, 2023

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book contains a fast-paced introduction to data-related tasks in preparation for training models on datasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset. Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading. Features: Covers extensive topics related to cleaning datasets and working with models Includes Python-based code samples and a separate chapter on Matplotlib and Seaborn Features companion files with source code, datasets, and figures from the book.

Seller Inventory # 9781683929529

Bibliographic Details

• Title: Managing Datasets and Models
• Publisher: De Gruyter
• Publication Date: 2023
• Language: English
• ISBN 10: 1683929527
• ISBN 13: 9781683929529
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book contains a fast-paced introduction to data-related tasks in preparation for training models on datasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset.

Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading.

Features:

• Covers extensive topics related to cleaning datasets and working with models
• Includes Python-based code samples and a separate chapter on Matplotlib and Seaborn
• Features companion files with source code, datasets, and figures from the book

About the Author

Campesato Oswald :

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Cruz and specializes in Deep Learning, NLP, Android, and Python. He is the author/co-author of over forty-five books including Data Science Fundamentals Pocket Primer, Python 3 for Machine Learning, and the Python Pocket Primer (Mercury Learning and Information).