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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
€37.99
Book Feb 2024 378 pages 1st Edition
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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

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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
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Part 1: What Data-Centric Machine Learning Is and Why We Need It Packt Packt
Part 1: What Data-Centric Machine Learning Is and Why We Need It
Chapter 1: Exploring Data-Centric Machine Learning Packt Packt
Chapter 1: Exploring Data-Centric Machine Learning
Understanding data-centric ML
Data-centric versus model-centric ML
The importance of quality data in ML
Summary
References
Chapter 2: From Model-Centric to Data-Centric – ML’s Evolution Packt Packt
Chapter 2: From Model-Centric to Data-Centric – ML’s Evolution
Exploring why ML development ended up being mostly model-centric
Unlocking the opportunity for small data ML
Why we need data-centric AI more than ever
Summary
References
Part 2: The Building Blocks of Data-Centric ML Packt Packt
Part 2: The Building Blocks of Data-Centric ML
Chapter 3: Principles of Data-Centric ML Packt Packt
Chapter 3: Principles of Data-Centric ML
Sometimes, all you need is the right data
Principle 1 – data should be the center of ML development
Principle 2 – leverage annotators and SMEs effectively
Principle 3 – use ML to improve your data
Principle 4 – follow ethical, responsible, and well-governed ML practices
Summary
References
Chapter 4: Data Labeling Is a Collaborative Process Packt Packt
Chapter 4: Data Labeling Is a Collaborative Process
Understanding the benefits of diverse human labeling
Understanding common challenges arising from human labelers
Designing a framework for high-quality labels
Measuring labeling consistency
Summary
References
Part 3: Technical Approaches to Better Data Packt Packt
Part 3: Technical Approaches to Better Data
Chapter 5: Techniques for Data Cleaning Packt Packt
Chapter 5: Techniques for Data Cleaning
The six key dimensions of data quality
Installing the required packages
Introducing the dataset
Ensuring the data is consistent
Checking that the data is unique
Ensuring that the data is complete and not missing
Ensuring that the data is valid
Ensuring that the data is accurate
Ensuring that the data is fresh
Summary
Chapter 6: Techniques for Programmatic Labeling in Machine Learning Packt Packt
Chapter 6: Techniques for Programmatic Labeling in Machine Learning
Technical requirements
Pattern matching
Database lookup
Boolean flags
Weak supervision
Semi-weak supervision
Slicing functions
Active learning
Transfer learning
Semi-supervised learning
Summary
Chapter 7: Using Synthetic Data in Data-Centric Machine Learning Packt Packt
Chapter 7: Using Synthetic Data in Data-Centric Machine Learning
Understanding synthetic data
Summary
References
Chapter 8: Techniques for Identifying and Removing Bias Packt Packt
Chapter 8: Techniques for Identifying and Removing Bias
The bias conundrum
Types of bias
The data-centric imperative
Case study
Summary
Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning Packt Packt
Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning
Importance of detecting rare events and edge cases in machine learning
Statistical methods
Anomaly detection
Data augmentation and resampling techniques
Cost-sensitive learning
Choosing evaluation metrics
Ensemble techniques
Summary
Part 4: Getting Started with Data-Centric ML Packt Packt
Part 4: Getting Started with Data-Centric ML
Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning Packt Packt
Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning
Solving six common ML challenges
Making data everyone’s business – our own experience
Summary
References
Index Packt Packt
Index
Why subscribe?
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