Matt Harrison - Machine Learning Pocket Reference [2019, EPUB, ENG] torrent

Книги и журналы » Компьютерная литература » Программирование (книги)

Machine Learning Pocket Reference

Год издания: 2019
Автор: Matt Harrison
Жанр или тематика: Natural Language Processing, Computer Neural Networks, Machine Theory

Издательство: O'Reilly Media, Inc.
ISBN: 9781492047544
Язык: Английский

Формат: EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 320

Описание: With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.

Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.

This pocket reference includes sections that cover: + Classification, using the Titanic dataset
+ Cleaning data and dealing with missing data
+ Exploratory data analysis
+ Common preprocessing steps using sample data
+ Selecting features useful to the model
+ Model selection
+ Metrics and classification evaluation
+ Regression examples using k-nearest neighbor, decision trees, boosting, and more
+ Metrics for regression evaluation
+ Clustering
+ Dimensionality reduction
+ Scikit-learn pipelines
[spoiler="Оглавление"]
Table of contents Preface
1. Introduction
2. Overview of the Machine Learning Process
3. Classification Walkthrough: Titanic Dataset
4. Missing Data
5. Cleaning Data
6. Exploring
7. Preprocess Data
8. Feature Selection
9. Imbalanced Classes
10. Classification
11. Model Selection
12. Metrics and Classification Evaluation
13. Explaining Models
14. Regression
15. Metrics and Regression Evaluation
16. Explaining Regression Models
17. Dimensionality Reduction
18. Clustering
19. Pipelines
Index
[/spoiler]