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    The Art of Machine Learning: A Hands-On Guide to Machine Learning with R

    The Art of Machine Learning by Matloff, Norman;

    A Hands-On Guide to Machine Learning with R

      • GET 13% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice GBP 47.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        24 287 Ft (23 131 Ft + 5% VAT)
      • Discount 13% (cc. 3 157 Ft off)
      • Discounted price 21 130 Ft (20 124 Ft + 5% VAT)

    24 287 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Publisher No Starch Press
    • Date of Publication 9 January 2024

    • ISBN 9781718502109
    • Binding Paperback
    • No. of pages272 pages
    • Size 235x178 mm
    • Language English
    • 937

    Categories

    Long description:

    Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You'll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbours method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You'll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you'll need in practice. Additional features: How to avoid common problems, such as dealing with 'dirty' data and factor variables with large numbers of levels; A look at typical misconceptions, such as dealing with unbalanced data; Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method; Dozens of illustrative examples involving real datasets of varying size and field of application; Standard R packages are used throughout, with a simple wrapper interface to provide convenient access. After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.

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