Data-Centric Machine Learning with Python
eBook Details:
- Paperback: 378 pages
- Publisher: WOW! eBook (February 29, 2024)
- Language: English
- ISBN-10: 1804618128
- ISBN-13: 978-1804618127
eBook Description:
Data-Centric Machine Learning with Python: Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python
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.
- 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
By the end of this Data-Centric Machine Learning with Python 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.
