Machine learning optimization data (173 Ergebnisse)

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Hardback. Zustand: Very Good. An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs?especially of key results?and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisionsTo apply effective data dimension reduction tools to enhance supervised learningTo describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.…

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Taschenbuch. Zustand: Neu. Neuware - Until recently, many people thought big data was a passing fad. 'Data science' was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you'll appreciate how data science is fundamentally altering our world, for better and for worse.Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you'll find out how far data science reaches.With this anthology, you'll learn how:- Analysts can now get results from their data queries in near real time- Indie manufacturers are blurring the lines between hardware and software- Companies try to balance their desire for rapid innovation with the need to tighten data security- Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center- CIOs have gradually evolved from order takers to business innovators- New analytics tools let businesses go beyond data analysis and straight to decision-makingMike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries. …

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Taschenbuch. Zustand: Neu. Neuware - Unlock the future of Artificial Intelligence with a groundbreaking guide on Automated Machine Learning. This book is tailored to revolutionize how you view and implement machine learning. With decades of ICT expertise distilled into actionable insights, embark on an educational journey that'll transform your understanding and leverage AI technology with precision. Delve deep into the core of AutoML, equipped with essential knowledge that bridges basic principles to advanced implementation. This guide isn't merely informative-it's designed to be transformative, turning beginners into informed users, and experts into pioneers. As the digital world accelerates, the need for efficient, automated intelligence grows keenly apparent. 'Automated Machine Learning' meets this demand, providing practical frameworks and strategies to harness AutoML's full potential. Dive into content that guides you through robust model selection and optimized hyperparameter tuning.>? Illuminate foundational concepts of Machine Learning? Navigate the latest AutoML frameworks with confidence? Master hyperparameter tuning and enhance model performance? Automate complex ML deployments effortlessly? Unearth transformative use cases across key industries>As a seasoned expert in ICT, the author brings a wealth of knowledge and hands-on experience. Benefit from insights honed over 5 years, ensuring you're guided by one of the industry's leading voices. Ready to advance your AI skills and elevate your understanding of machine learning Seize this opportunity-secure your copy today and step forward into the exciting realm of Automated Machine Learning.…

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Hardback. Zustand: New. An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs-especially of key results-and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisionsTo apply effective data dimension reduction tools to enhance supervised learningTo describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.…

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