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Data-Centric Machine Learning with Python

You're reading from  Data-Centric Machine Learning with Python

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781804618127
Pages 378 pages
Edition 1st Edition
Languages
Authors (3):
Jonas Christensen Jonas Christensen
Profile icon Jonas Christensen
Nakul Bajaj Nakul Bajaj
Profile icon Nakul Bajaj
Manmohan Gosada Manmohan Gosada
Profile icon Manmohan Gosada
View More author details

Table of Contents (17) Chapters

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

Technical requirements

To execute the code examples provided in this chapter on programmatic labeling techniques, ensure that you have the following technical prerequisites installed in your Python environment:

Python version

The examples in this chapter require Python version 3.7 or higher. You can check your Python version by running the following:

import sys
print(sys.version)

We recommend using the Jupyter Notebook integrated development environment (IDE) for an interactive and organized coding experience. If you don’t have it installed, you can install it using this line:

pip install jupyter

Launch Jupyter Notebook with the following command:

jupyter notebook

Library requirements

Ensure that the following Python packages are installed in your environment. You can install them using the following commands:

pip install snorkel
pip install scikit-learn
pip install Pillow
pip install tensorflow
pip install pandas
pip install numpy

Additionally,...

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