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    Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Autorzy:
    Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
    Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis Huy Hoang Nguyen, Paul N Adams, Stuart J Miller - okładka ebooka

    Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis Huy Hoang Nguyen, Paul N Adams, Stuart J Miller - okładka ebooka

    Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis Huy Hoang Nguyen, Paul N Adams, Stuart J Miller - okładka audiobooka MP3

    Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis Huy Hoang Nguyen, Paul N Adams, Stuart J Miller - okładka audiobooks CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    420
    Dostępne formaty:
    PDF
    ePub
    +139 pkt

    Ebook

    139,00 zł

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    Do przechowalni

    The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.

    This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more.

    By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.

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    O autorach ebooka

    Huy Hoang Nguyen is a Mathematician and a Data Scientist with far-ranging experience, championing advanced mathematics and strategic leadership, and applied machine learning research. He holds a Master’s in Data Science and a PhD in Mathematics. His previous work was related to Partial Differential Equations, Functional Analysis and their applications in Fluid Mechanics. He transitioned from academia to the healthcare industry and has performed different Data Science projects from traditional Machine Learning to Deep Learning.
    Paul Adams is a Data Scientist with a background primarily in the healthcare industry. Paul applies statistics and machine learning in multiple areas of industry, focusing on projects in process engineering, process improvement, metrics and business rules development, anomaly detection, forecasting, clustering and classification. Paul holds a Master of Science in Data Science from Southern Methodist University.
    Stuart Miller is a Machine Learning Engineer with degrees in Data Science, Electrical Engineering, and Engineering Physics. Stuart has worked at several Fortune 500 companies, including Texas Instruments and StateFarm, where he built software that utilized statistical and machine learning techniques. Stuart is currently an engineer at Toyota Connected helping to build a more modern cockpit experience for drivers using machine learning.

    Oceny i opinie klientów: Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis Huy Hoang Nguyen, Paul N Adams, Stuart J Miller (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.

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