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ISBN-13:9781804618127
Book Title:Data-Centric Machine Learning with Python
Number of Pages:300 Pages
Publication Name:Data-Centric Machine Learning with Python : The Ultimate Guide to Engineering and Deploying High-Quality Models Based on Good Data
Language:English
Publisher:Packt Publishing, The Limited
Publication Year:2024
Subject:General,Data Processing
Type:Textbook
Item Length:92.5 in
Subject Area:Computers,Science
Author:Nakul Bajaj,Manmohan Gosada,Jonas Christensen
Item Width:75 in
Format:Trade Paperback
Data-Centric Machine Learning with Python by Jonas Christensen, Nakul Bajaj, Manmohan Gosada, Kirk D. Borne Estimated delivery 3-12 business days Format Paperback Condition Brand New Description This book is a compilation of essential background knowledge, tools, techniques, and applied examples needed to implement data-centric ML. Learn how to apply a best-practice data-centric approach in your work with this informative guide. Publisher Description Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using PythonKey FeaturesGrasp the principles of data centricity and apply them to real-world scenariosGain experience with quality data collection, labeling, and synthetic data creation using PythonDevelop essential skills for building reliable, responsible, and ethical machine learning solutionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn 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 learnUnderstand the impact of input data quality compared to model selection and tuningRecognize the crucial role of subject-matter experts in effective model developmentImplement data cleaning, labeling, and augmentation best practicesExplore common synthetic data generation techniques and their applicationsApply synthetic data generation techniques using common Python packagesDetect and mitigate bias in a dataset using best-practice techniquesUnderstand the importance of reliability, responsibility, and ethical considerations in ML/AIWho this book is forThis 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. Author Biography Jonas Christensen has spent his career leading data science functions across multiple industries. He is an international keynote speaker, postgraduate educator, and advisor in the fields of data science, analytics leadership, and machine learning and host of the Leaders of Analytics podcast. Nakul Bajaj is a data scientist, MLOps engineer, educator and mentor, helping students and junior engineers navigate their data journey. He has a strong passion for MLOps, with a focus on reducing complexity and delivering value from machine learning use-cases in business and healthcare. Manmohan Gosada is a seasoned professional with a proven track record in the dynamic field of data science. With a comprehensive background spanning various data science functions and industries, Manmohan has emerged as a leader in driving innovation and delivering impactful solutions. He has successfully led large-scale data science projects, leveraging cutting-edge technologies to implement transformative products. With a postgraduate degree, he is not only well-versed in the theoretical foundations of data science but is also passionate about sharing insights and knowledge. A captivating speaker, he engages audiences with a blend of expertise and enthusiasm, demystifying complex concepts in the world of data science. Details ISBN 1804618128 ISBN-13 9781804618127 Title Data-Centric Machine Learning with Python Author Jonas Christensen, Nakul Bajaj, Manmohan Gosada, Kirk D. Borne Format Paperback Year 2024 Pages 378 Publisher Packt Publishing Limited GE_Item_ID:158690984; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items – you’re bound to find what you want, at a price you’ll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request “Return Authorisation” before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer’s risk. 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