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    Applied Supervised Learning with R. Use machine learning libraries of R to build models that solve business problems and predict future trends

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Karthik Ramasubramanian, Jojo Moolayil
    Applied Supervised Learning with R. Use machine learning libraries of R to build models that solve business problems and predict future trends Karthik Ramasubramanian, Jojo Moolayil - okladka książki

    Applied Supervised Learning with R. Use machine learning libraries of R to build models that solve business problems and predict future trends Karthik Ramasubramanian, Jojo Moolayil - okladka książki

    Applied Supervised Learning with R. Use machine learning libraries of R to build models that solve business problems and predict future trends Karthik Ramasubramanian, Jojo Moolayil - audiobook MP3

    Applied Supervised Learning with R. Use machine learning libraries of R to build models that solve business problems and predict future trends Karthik Ramasubramanian, Jojo Moolayil - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    502
    Dostępne formaty:
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    Ebook (29,90 zł najniższa cena z 30 dni)

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    Do przechowalni

    R provides excellent visualization features that are essential for exploring data before using it in automated learning.

    Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.

    By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.

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    O autorach książki

    Karthik Ramasubramanian completed his M.Sc. in Theoretical Computer Science at PSG College of Technology, India, where he pioneered the application of machine learning, data mining, and fuzzy logic in his research work on computer and network security. He has over seven years' experience of leading data science and business analytics in retail, Fast-Moving Consumer Goods, e-commerce, information technology, and the hospitality industry for multinational companies and unicorn start-ups.
    Jojo Moolayil is a data scientist, living in Bengaluru—the silicon valley of India. With over

    4 years of industrial experience in Decision Science and IoT, he has worked with industry leaders on high impact and critical projects across multiple verticals. He is currently associated with GE, the pioneer and leader in data science for Industrial IoT.

    Jojo was born and raised in Pune, India and graduated from University of Pune with a

    major in information technology engineering. With a vision to solve problems at scale, Jojo found solace in decision science and learnt to solve a variety of problems across multiple industry verticals early in his career. He started his career with Mu Sigma Inc., the world's largest pure play analytics provider where he worked with the leaders of many fortune 50 clients. With the passion to solve increasingly complex problems, Jojo touch based with Internet of Things and found deep interest in the very promising area of consumer and industrial IoT. One of the early enthusiasts to venture into IoT analytics, Jojo converged his learnings from decision science to bring the problem solving frameworks and his learnings from data and decision science to IoT.

    To cement his foundations in industrial IoT and scale the impact of the problem solving

    experiments, he joined a fast growing IoT Analytics startup called Flutura based in

    Bangalore and headquartered in the valley. Flutura focuses exclusively on Industrial IoT

    and specializes in analytics for M2M data. It is with Flutura, where Jojo reinforced his

    problem solving skills for M2M and Industrial IoT while working for the world's leading

    manufacturing giant and lighting solutions providers. His quest for solving problems at

    scale brought the 'product' dimension in him naturally and soon he also ventured into

    developing data science products and platforms.

    After a short stint with Flutura, Jojo moved on to work with the leaders of Industrial IoT,

    that is, G.E. in Bangalore, where he focused on solving decision science problems for

    Industrial IoT use cases. As a part of his role in GE, Jojo also focuses on developing data

    science and decision science products and platforms for Industrial IoT.

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