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    Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Benjamin Johnston, Ishita Mathur
    Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning Benjamin Johnston, Ishita Mathur - okladka książki

    Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning Benjamin Johnston, Ishita Mathur - okladka książki

    Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning Benjamin Johnston, Ishita Mathur - audiobook MP3

    Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning Benjamin Johnston, Ishita Mathur - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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    Stron:
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    Dostępne formaty:
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    Do przechowalni

    Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.

    With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.

    This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.

    By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

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    O autorach książki

    Benjamin Johnston zajmuje się zaawansowaną analizą danych w branży medycznej. Interesuje się uczeniem maszynowym, przetwarzaniem obrazów i sieciami neuronowymi.

    Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.

    Benjamin Johnston, Ishita Mathur - pozostałe książki

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