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    Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

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

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

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

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

    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    370
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    149,00 zł

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    The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems.

    Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, 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.

    This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data.

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

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    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.

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