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    scikit-learn: Machine Learning Simplified. Implement scikit-learn into every step of the data science pipeline Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling

    (ebook) (audiobook) (audiobook) Książka w języku 1
    scikit-learn: Machine Learning Simplified. Implement scikit-learn into every step of the data science pipeline Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling - okladka książki

    scikit-learn: Machine Learning Simplified. Implement scikit-learn into every step of the data science pipeline Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling - okladka książki

    scikit-learn: Machine Learning Simplified. Implement scikit-learn into every step of the data science pipeline Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling - audiobook MP3

    scikit-learn: Machine Learning Simplified. Implement scikit-learn into every step of the data science pipeline Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling - audiobook CD

    Autorzy:
    Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Dostępne formaty:
         PDF
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         Mobi
    Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning.

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    O autorach książki

    Guillermo Moncecchi is a Natural Language Processing researcher at the Universidad de la Repblica of Uruguay. He received a PhD in Informatics from the Universidad de la Repblica, Uruguay and a Ph.D in Language Sciences from the Universit Paris Ouest, France. He has participated in several international projects on NLP. He has almost 15 years of teaching experience on Automata Theory, Natural Language Processing, and Machine Learning. He also works as Head Developer at the Montevideo Council and has lead the development of several public services for the council, particularly in the Geographical Information Systems area. He is one of the Montevideo Open Data movement leaders, promoting the publication and exploitation of the city's data.
    Raúl Garreta is a Computer Engineer with much experience in the theory and application of Artificial Intelligence (AI), where he specialized in Machine Learning and Natural Language Processing (NLP).
    He has an entrepreneur profile with much interest in the application of science, technology, and innovation to the Internet industry and startups. He has worked in many software companies, handling everything from video games to implantable medical devices.
    In 2009, he co-founded Tryolabs with the objective to apply AI to the development of intelligent software products, where he performs as the CTO and Product Manager of the company. Besides the application of Machine Learning and NLP, Tryolabs' expertise lies in the Python programming language and has been catering to many clients in Silicon Valley. Raul has also worked in the development of the Python community in Uruguay, co-organizing local PyDay and PyCon conferences.
    He is also an assistant professor at the Computer Science Institute of Universidad de la República in Uruguay since 2007, where he has been working on the courses of Machine Learning, NLP, as well as Automata Theory and Formal Languages. Besides this, he is finishing his Masters degree in Machine Learning and NLP. He is also very interested in the research and application of Robotics, Quantum Computing, and Cognitive Modeling. Not only is he a technology enthusiast and science fiction lover (geek) but also a big fan of arts, such as cinema, photography, and painting.
    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.
    Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's degree from the University of North Carolina.

    Guillermo Moncecchi, Raul G Tompson, Trent Hauck, Gavin Hackeling - pozostałe książki

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