New. New Book; Fast Shipping from UK; Not signed; Not First Edition; The Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods. ISBN 1801819629 9781801819626 [GB]

ISBN10: 1801819629, ISBN13: 9781801819626, [publisher: Packt Publishing] Softcover Buy with confidence! Book is in good condition with minor wear to the pages, binding, and minor marks within
[Amherst, NY, U.S.A.] [Publication Year: 2021]

ISBN10: 1801819629, ISBN13: 9781801819626, [publisher: Packt Publishing] Softcover Book is in Used-LikeNew condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear.
[Hawthorne, CA, U.S.A.] [Publication Year: 2021]

ISBN10: 1801819629, ISBN13: 9781801819626, [publisher: Packt Publishing] Softcover Book is in NEW condition.
[Hawthorne, CA, U.S.A.] [Publication Year: 2021]

ISBN10: 1801819629, ISBN13: 9781801819626, [publisher: Packt Publishing] Softcover nach der Bestellung gedruckt Neuware - Printed after ordering - Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features:Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description:Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right ...
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