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    The Data Science Workshop. Learn how you can build machine learning models and create your own real-world data science projects - Second Edition Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

    (ebook) (audiobook) (audiobook) Książka w języku 1
    The Data Science Workshop. Learn how you can build machine learning models and create your own real-world data science projects - Second Edition Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare - okladka książki

    The Data Science Workshop. Learn how you can build machine learning models and create your own real-world data science projects - Second Edition Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare - okladka książki

    The Data Science Workshop. Learn how you can build machine learning models and create your own real-world data science projects - Second Edition Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare - audiobook MP3

    The Data Science Workshop. Learn how you can build machine learning models and create your own real-world data science projects - Second Edition Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare - audiobook CD

    Autorzy:
    Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    824
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.

    The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.

    Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.

    By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.

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    O autorach książki

    Anthony So is a renowned leader in data science. He has extensive experience in solving complex business problems using advanced analytics and AI in different industries including financial services, media, and telecommunications. He is currently the chief data officer of one of the most innovative fintech start-ups. He is also the author of several best-selling books on data science, machine learning, and deep learning. He has won multiple prizes at several hackathon competitions, such as Unearthed, GovHack, and Pepper Money. Anthony holds two master's degrees, one in computer science and the other in data science and innovation.
    Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.
    Robert Thas John is a Google developer expert in machine learning. His day job involves working as a data engineer on the Google Cloud Platform by building, training, and deploying large-scale machine learning models. He also makes decisions about how to store and process large amounts of data. He has more than 10 years of experience in building enterprise-grade solutions and working with data. He spends his free time learning or contributing to the developer community. He frequently travels to speak at technology events or to mentor developers. He also writes a blog on data science.
    Andrew David Worsley is an independent consultant and educator with expertise in the areas of machine learning, statistics, cloud computing, and artificial intelligence. He has practiced data science in several countries across a multitude of industries including retail, financial services, marketing, resources, and healthcare.
    Dr. Samuel Asare is a professional engineer with enthusiasm for Python programming, research, and writing. He is highly skilled in applying data science methods to the extraction of useful insights from large data sets. He possesses solid skills in project management processes. Samuel has previously held positions, in industry and academia, as a process engineer and a lecturer of materials science and engineering respectively. Presently, he is pursuing his passion for solving industry problems, using data science methods, and writing.

    Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare - pozostałe książki

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