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Data-Centric Machine Learning with Python
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

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Profile Icon Jonas Christensen Profile Icon Manmohan Gosada Profile Icon Nakul Bajaj
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€37.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (5 Ratings)
Paperback Feb 2024 378 pages 1st Edition
eBook
€8.99 €29.99
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€37.99
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Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Jonas Christensen Profile Icon Manmohan Gosada Profile Icon Nakul Bajaj
Arrow right icon
€37.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (5 Ratings)
Paperback Feb 2024 378 pages 1st Edition
eBook
€8.99 €29.99
Paperback
€37.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €29.99
Paperback
€37.99
Subscription
Free Trial
Renews at €18.99p/m

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Data-Centric Machine Learning with Python

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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.

Who is this book for?

This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.

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

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

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Table of Contents

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

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(5 Ratings)
5 star 60%
4 star 40%
3 star 0%
2 star 0%
1 star 0%
Steven Fernandes Apr 12, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This insightful book dives deep into the essentials of machine learning and artificial intelligence, stressing the importance of data quality, expert involvement, and ethical considerations above complex algorithms. It offers practical advice on data handling, including cleaning, labeling, and augmentation, and provides a thorough guide to synthetic data generation with Python. The book is particularly strong on addressing bias, presenting effective strategies for creating fair and equitable AI systems. It rounds off with a critical look at the ethics and responsibilities in AI development. A must-read for anyone in the field, blending technical guidance with a strong ethical framework.
Amazon Verified review Amazon
Advitya Gemawat Apr 18, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Going through the book honestly made me walk back memory lane when I took a 'Practice and Applications of Data Science' class back in sophomore year of college.- 📈 **Data Preprocessing**: The book covers various Data Imputation techniques used to handling missing data. It also discusses different ways to detect and handle outliers, including Z-score, IQR method, and the corresponding use of scatter plots and box plots.- 🧠 **Model Selection**: There's a nice walkthrough of various traditional #ML models like Linear & Logistic regression, Decision Trees, and SVMs, along with their assumptions, pros & cons, and code snippets for using these models with scikit-learn. The bullet points in Chapter 9 can especially be good to use for interview prep for college students- 📊 **Feature Engineering**: The book covers foundational techniques like binning, log transform, one-hot encoding, and interaction features, along with overarching strategies such as Active Learning, Weak Supervision, and Semi-supervised learning.But my favorite part of the book was the **Data-Centricity** aspect. Quite frankly, I initially found the book title a bit vague as data-centricity in ML sounded obvious to me. But this is one of the few books I even ended up reading the 'Foreword' of, which I've usually skipped. And here's why -The Foreword combined with the last chapter had an well-intentional storyline of introducing the philosophical foundations of how data was collected throughout history, *how* (not just why) data is the main and most important fabric of the ML lifecycle, and even touching upon Responsible #AI at the end.This storyline can be quite beneficial for folks newly getting into the ML space - for a traditional model or an LLM, groundedness in high-quality data is the most important and effective driver of the lifecycle.The book reads like a more relevant resource for early-in-career professionals looking to enter the AI industry, folks looking to strengthen their fundamentals and use for interview prep, and college students with a basic programming background.
Amazon Verified review Amazon
H2N Apr 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book discusses about improving data quality in order to get robust, fair, and interpretable ML models. It challenges the traditional focus on algorithms by shifting towards data-centric methods. This book provides lots of practical examples using modern techniques, emphasizing collaboration between data scientists and domain experts to refine data quality, making it an essential guide for advancing in AI.
Amazon Verified review Amazon
Om S Apr 12, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
When I first started out in machine learning, I was completely absorbed in tweaking algorithms and tuning models, always hunting for that slight improvement in accuracy. However, I quickly realized that no matter how much I optimized the models, the real barrier to superior performance was the quality of the underlying data. This revelation shifted my focus entirely and led me to explore data-centric approaches in machine learning—a journey that transformed my projects.This book provides a thorough guide to data-centric machine learning, emphasizing the critical role of high-quality data over merely adjusting models. It introduces the impactful concept of 'small data' and how it can revolutionize ML/AI projects. Through practical strategies for enhancing data collection, labeling, and augmentation, the book ensures readers can refine their datasets effectively. It explores the importance of synthetic data and the necessity of expert involvement in data labeling, equipping professionals with tools to boost their ML practices. The book also addresses how to identify and eliminate biases and stresses the ethical dimensions of machine learning, offering a comprehensive view of both the challenges and solutions in the field. Ideal for data science professionals and enthusiasts, this book serves as an essential resource for anyone eager to prioritize data quality in their machine learning projects.
Amazon Verified review Amazon
Amazon Customer Mar 24, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book adeptly explores the critical importance of data quality in ML projects, providing practical insights and techniques for creating reliable datasets. From data labeling to synthetic data generation, the book equips readers with the essential skills to enhance their ML practices. With a focus on ethics and responsibility, this book serves as a valuable resource for data professionals and ML enthusiasts seeking to elevate their projects through data-centric approaches.
Amazon Verified review Amazon
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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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