Menu

Szukaj

Zamknij

    Zaloguj się

    Biblioteka

    0,00 zł (0)

    Koszyk

    ODBIERZ TWÓJ BONUS :: »

    Codziennie inna książka za 9,90zł

    Codziennie inny audiobook za 12,90zł

    Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Autor:
    Ben Auffarth
    Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition Ben Auffarth - okładka ebooka

    Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition Ben Auffarth - okładka ebooka

    Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition Ben Auffarth - okładka audiobooka MP3

    Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition Ben Auffarth - okładka audiobooks CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    392
    Dostępny format:
    ePub
    +139 pkt
    The Python time-series ecosystem is a huge and challenging topic to tackle, especially for time series since there are so many new libraries and models. Machine Learning for Time Series, Second Edition, aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and helping you build better predictive systems.

    This fully updated second edition starts by re-introducing the basics of time series and then helps you get to grips with traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will gain a deeper understanding of loading time-series datasets from any source and a variety of models, such as deep learning recurrent neural networks, causal convolutional network models, and gradient boosting with feature engineering. This book will also help you choose the right model for the right problem by explaining the theory behind several useful models. New updates include a chapter on forecasting and extracting signals on financial markets and case studies with relevant examples from operations management, digital marketing, and healthcare.

    By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time series.

    Wybrane bestsellery

    O autorze ebooka

    Ben Auffarth is a full-stack data scientist who has >15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience from one of Europe's top engineering universities, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds of thousands of transactions per day, and trained neural networks on millions of text documents. In his work, he often notices a lack of appreciation for the importance of time-related factors, a deficit he wanted to address in this book. He co-founded and is the former president of Data Science Speakers, London.

    Oceny i opinie klientów: Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition Ben Auffarth (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

    Zamknij

    Wybierz metodę płatności

    Zamknij Pobierz aplikację mobilną Ebookpoint