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Data-Centric Machine Learning with Python

You're reading from   Data-Centric Machine Learning with Python The ultimate guide to engineering and deploying high-quality models based on good data

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781804618127
Length 378 pages
Edition 1st Edition
Languages
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Authors (3):
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Jonas Christensen Jonas Christensen
Author Profile Icon Jonas Christensen
Jonas Christensen
Manmohan Gosada Manmohan Gosada
Author Profile Icon Manmohan Gosada
Manmohan Gosada
Nakul Bajaj Nakul Bajaj
Author Profile Icon Nakul Bajaj
Nakul Bajaj
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: What Data-Centric Machine Learning Is and Why We Need It FREE CHAPTER
2. Chapter 1: Exploring Data-Centric Machine Learning 3. Chapter 2: From Model-Centric to Data-Centric – ML’s Evolution 4. Part 2: The Building Blocks of Data-Centric ML
5. Chapter 3: Principles of Data-Centric ML 6. Chapter 4: Data Labeling Is a Collaborative Process 7. Part 3: Technical Approaches to Better Data
8. Chapter 5: Techniques for Data Cleaning 9. Chapter 6: Techniques for Programmatic Labeling in Machine Learning 10. Chapter 7: Using Synthetic Data in Data-Centric Machine Learning 11. Chapter 8: Techniques for Identifying and Removing Bias 12. Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning 13. Part 4: Getting Started with Data-Centric ML
14. Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning 15. Index 16. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “We will call the loan_dataset.csv file and will save it in the same directory, from where we will run this example.”

A block of code is set as follows:

import pandas as pd
import os
FILENAME = "./loan_dataset.csv"
DATA_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls"

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Biases in machine learning can take many forms, hence we categorized these biases into two main types, easy to identify biases and difficult to identify biases.”

Tips or important notes

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