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    Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu
    Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu - okladka książki

    Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu - okladka książki

    Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu - audiobook MP3

    Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    416
    Dostępne formaty:
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         Mobi

    Ebook (29,90 zł najniższa cena z 30 dni)

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    Do przechowalni

    With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way.

    Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them.

    By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.

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    O autorach książki

    Brindha Priyadarshini Jeyaraman is a senior data scientist at AIDA Technologies. She has completed her M.Tech in knowledge engineering with a gold medal from the National University of Singapore. She has more than 10 years of work experience and she is an expert in understanding business problems, and designing and implementing solutions using machine learning. She has worked on several real data science projects in the insurance and finance domain.
    Ludvig Renbo Olsen, BSc in Cognitive Science from Aarhus University, is the author of multiple R packages, such as groupdata2 and cvms. With 4 years of R and Python experience, including working as a machine learning researcher at the Danish startup UNSILO, he is passionate about creating tools and tutorials for students and scientists. Guided by Effective Altruism, he intends to positively impact the world through his career.
    Monicah Wambugu is the lead Data Scientist at Loanbee, a financial technology company that offers micro-loans by leveraging on data, machine learning and analytics to perform alternative credit scoring. She is a graduate student at the School of Information at UC Berkeley Masters in Information Management and Systems. Monicah is particularly interested in how data science and machine learning can be used to design products and applications that respond to the behavioral and socio-economic needs of target audiences.

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