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    scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition Julian Avila, Trent Hauck

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition Julian Avila, Trent Hauck - okladka książki

    scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition Julian Avila, Trent Hauck - okladka książki

    scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition Julian Avila, Trent Hauck - audiobook MP3

    scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition Julian Avila, Trent Hauck - audiobook CD

    Autorzy:
    Julian Avila, Trent Hauck
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    374
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.

    The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model.

    By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.

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    O autorach książki

    Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab. He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.
    Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.

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