Apress - Advanced Data Analytics Using Python: With Machine Learning, Deep Learning And NLP Examples (2018 EN)

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    Author: Sayan Mukhopadhyay
    Full Title: Advanced Data Analytics Using Python: With Machine Learning, Deep Learning And NLP Examples
    Publisher: Apress; 1st ed. edition (March 29, 2018)
    Year: 2018
    ISBN-13: 9781484234501 (978-1-4842-3450-1), 9781484234495 (978-1-4842-3449-5)
    ISBN-10: 1484234502, 1484234499
    Pages: 186
    Language: English
    Genre: Educational: Data analysis
    File type: EPUB (True), PDF (True), Code Files
    Quality: 10/10
    Price: 37.44 €


    Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.

    After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects.


    Learn:
    ✓ Work with data analysis techniques such as classification, clustering, regression, and forecasting
    ✓ Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
    ✓ Examine the different big data frameworks, including Hadoop and Spark
    ✓ Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP

    Features:
    ✓ Contains practical real-world examples of data analytics
    ✓ Covers a wide spectrum from basic statistics to ETL, deep learning and IoT
    ✓ Gives an idea of every technical aspect of an industrial analytics project

    Who This Book Is For:
    Data scientists and software developers interested in the field of data analytics.

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