9781804612415_

Be part of the data-centric revolution and become proficient in the concepts, techniques, and algorithms shaping the future of AI and ML advancement with Python.

Key Features

Get up and running with different data management strategy
Discover the advantages of employing a data-centric approach for improved outcomes
Understand various facets of implementing data-centric approach in machine learning

Book Description

This book caters to data science professionals seeking insights into data-centricity, highlighting its advantages over a model-centric approach and guiding readers on implementing a best-practice data-centric methodology. Its also intended for other data professionals and senior leaders aiming to enhance data quality, foster "small data" ML/AI opportunities, and explore tools and techniques within their organizations. The concept of data centricity is relatively new in the realm of data science, and comprehensive learning resources on this topic are scarce. This book offers a unique, comprehensive overview of data-centric ML, presenting both technical and non-technical methodologies for creating more profound and accurate datasets through practical application.

What you will learn

Learn the principles of data centricity and how they apply to real-world scenarios
Understand the significance of subject-matter experts in effective model development
Implement best-practice data cleaning, labelling and augmentation
Learn common synthetic data generation techniques and their application
Get well versed with applying these techniques using common Python packages
Detect and correct bias in a dataset using best-practice techniques
Acknowledge the importance of ethical and responsible ML/AI

Who this book is for

This book caters to data science professionals seeking insights into data-centricity, its advantages over a model-centric approach, and methods for implementing a proficient data-centric approach in their field. Its also designed for senior leaders eager to delve into tools and strategies for enhancing data quality and fostering "small data" ML/AI opportunities within their organizations. Prior familiarity with the ML/AI process and a foundational understanding of statistical methods and Python are recommended for readers aiming to navigate through the books more technical sections.

...(*_*)9781804612415_

Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python

Key Features

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

Book 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, youll 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, youll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, youll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, youll 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

Who this book is 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.

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Data-Centric Machine Learning with Python

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