- Добавил: literator
- Дата: 6-03-2023, 11:45
- Комментариев: 0
Название: Managing Datasets and Models
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2023
Страниц: 387
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
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. Next, you will see other classification algorithms (on the same dataset), such as decision trees, random forests, SVMs (support vector machines), and Naive Bayes simply by modifying three lines of code. 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.
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2023
Страниц: 387
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
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. Next, you will see other classification algorithms (on the same dataset), such as decision trees, random forests, SVMs (support vector machines), and Naive Bayes simply by modifying three lines of code. 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.