R v krishnan (69 Ergebnisse)

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Paperback. Zustand: New. This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive  integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average),  ARMA (autoregressive moving-average) and ARIMA (autoregressive  integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.…

Zustand: New. 2022. 1st ed. paperback. . . . . .

Paperback. Zustand: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.

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Hardcover. Zustand: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

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Paperback. Zustand: New. 1st. This book offers an overview of India's emerging digital economy and the resulting challenges and opportunities for urban workplaces. It examines contemporary economic and social transformations in India by focusing on how new technologies and policies are shaping urban work practices and patterns. The book emphasizes inclusive and equitable practices that consider the needs of the formal and informal sector workforce as essential to India's urban development. Drawing on cross-disciplinary frameworks, it examines key issues related to work trends in the Indian urban economy and its digital landscapes, including Industry 4.0 and technology-labour nexus, smart cities and innovation, urbanism and consumerism, workplace transitions such as service industry and remote work, digital divide, skill development initiatives, and the impact of socio-economic inequalities and disruptions. The authors provide perspectives on the digital future of urban work in India and other emerging economies in the post-COVID-19 phase, and underscore the importance of enacting balanced policies, remodelling institutions, and equipping the labour force for adapting to new demands related to future employability and investments.This book will interest students, teachers, and researchers of urban studies, urban sociology, sociology of work, labour studies, human and urban geography, economic geography, urban economics, development studies, urban development and planning, public policy, regional planning, politics of urban development, social and cultural change, urban sustainability, environmental studies, management studies, South Asian Studies, and Global South studies. It will also be useful to policymakers, non-governmental organizations, activists, and those interested in India and the future of the global economy.…

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Paperback. Zustand: Brand New. 226 pages. 9.19x6.13x0.51 inches. In Stock.

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Paperback. Zustand: New. This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive  integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average),  ARMA (autoregressive moving-average) and ARIMA (autoregressive  integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.…