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Data-Centric Machine Learning with Python

Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data

By Jonas Christensen , Nakul Bajaj , Manmohan Gosada
$39.99 $27.98
Book Feb 2024 378 pages 1st Edition
eBook
$39.99 $27.98
Print
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$15.99 Monthly
eBook
$39.99 $27.98
Print
$49.99
Subscription
$15.99 Monthly

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Product Details


Publication date : Feb 29, 2024
Length 378 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781804618127
Category :
Concepts :
toc View table of contents toc Preview Book toc Download Code

Key benefits

  • Grasp the principles of data centricity and apply them to real-world scenarios
  • Gain experience with quality data collection, labeling, and synthetic data creation using Python
  • Develop essential skills for building reliable, responsible, and ethical machine learning solutions
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.

What you will learn

Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI

What do you get with eBook?

Feature icon Instant access to your Digital eBook purchase
Feature icon Download this book in EPUB and PDF formats
Feature icon Access this title in our online reader with advanced features
Feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details


Publication date : Feb 29, 2024
Length 378 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781804618127
Category :
Concepts :

Table of Contents

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

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