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    Data Science Projects with Python. A case study approach to gaining valuable insights from real data with machine learning - Second Edition Stephen Klosterman

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Data Science Projects with Python. A case study approach to gaining valuable insights from real data with machine learning - Second Edition Stephen Klosterman - okladka książki

    Data Science Projects with Python. A case study approach to gaining valuable insights from real data with machine learning - Second Edition Stephen Klosterman - okladka książki

    Data Science Projects with Python. A case study approach to gaining valuable insights from real data with machine learning - Second Edition Stephen Klosterman - audiobook MP3

    Data Science Projects with Python. A case study approach to gaining valuable insights from real data with machine learning - Second Edition Stephen Klosterman - audiobook CD

    Autor:
    Stephen Klosterman
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    432
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Zostało Ci na świąteczne zamówienie opcje wysyłki »
    If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.

    In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.

    You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.

    Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.

    By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.

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    O autorze książki

    Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.

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