Statistical machine learning models (42 Ergebnisse)

Paperback. Zustand: Fair. This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.…

Zustand: Good. 1st Edition. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: New.

paperback. Zustand: Very Good.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: New.

Hardback. Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: New. In English.

Zustand: As New. Unread book in perfect condition.

Zustand: New. 2019. First Edition. paperback. . . . . .

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - Time series forecasting is driving decision-making in everything from financial markets to supply chain logistics. This book provides a hands-on roadmap to mastering this technology, bridging the gap between classical statistical rigor and cutting-edge artificial intelligence. Understand time series fundamentals by exploring decomposition, stationarity, and ACF/PACF analysis before mastering preprocessing and feature engineering. You will build foundational ARIMA, SARIMA, and Holt-Winters' models before pivoting to machine learning with XGBoost and Scikit-learn. The journey accelerates into deep learning, designing RNNs, LSTMs, and hybrid CNN-LSTM architectures for univariate and multivariate forecasting. After exploring advanced VAR and VECM models, you will implement walk-forward validation and professional error metrics. The final sections cover scalability and MLOps, teaching you to handle big data with Dask and deploy production-ready models via FastAPI and Apache Kafka. By the end of this book, you will be a competent practitioner capable of building high-performance forecasting pipelines for stock prices, demand, and sensor data. You will possess the technical expertise to deploy scalable, ethical, and accurate models in real-world cloud environments with confidence.What you will learn? Diagnose trend and seasonality using Statsmodels stationarity.? Build ARIMA/SARIMA and smoothing models using Statsmodels.? Engineer lag, rolling, and calendar-based forecasting features.? Deploy FastAPI pipelines and monitor Kafka drift.? Build LSTM and GRU architectures with TensorFlow.? Backtest, compare, and ensemble models with confidence.? Deploy, monitor, and retrain forecasting pipelines at scale.Who this book is forThis book is designed for data scientists, machine learning engineers, and analysts mastering temporal data. Proficiency in Python and basic statistics is required, while experience with cloud deployment or deep learning helps professional engineers scale models using the featured technical frameworks.Table of Contents1. Introduction to Time Series Data and Analysis2. Data Pre-processing and Feature Engineering3. Exploratory and Statistical Analysis of Time Series4. Autoregressive Models5. Moving Average and ARMA Models6. ARIMA and SARIMA Models7. Exponential Smoothing Methods8. Feature-based Machine Learning for Time Series Forecasting9. Introduction to Deep Learning for Time Series10. Building and Training LSTM Models for Time Series11. Advanced Deep Learning Architectures and Multivariate Forecasting12. Multivariate Time Series Forecasting13. Model Evaluation, Selection, and Ensembling14. Forecasting at Scale and Model Deployment15. Time Series Forecasting in Practice.…

Zustand: New. 2019. First Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 638 pages. 10.00x7.00x1.50 inches. In Stock.

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Zustand: As New. Unread book in perfect condition.

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Zustand: New.

Hardback. Zustand: New.

Paperback. Zustand: Brand New. 489 pages. 9.00x6.00x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Forecasting Models for Non-Scheduled Air Transportation | Statistical Analysis, Machine Learning Methods and Hybrid Forecasting Approaches | Dashqin Nazarli | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209788079 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

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Taschenbuch. Zustand: Neu. Advanced R Statistical Programming and Data Models | Analysis, Machine Learning, and Visualization | Matt Wiley (u. a.) | Taschenbuch | xx | Englisch | 2019 | Apress | EAN 9781484228715 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …