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HKSYU Library

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    LEADER 02603cam a2200385 i 4500
    001
    991001661459707546
    005
    20240831160655.0
    008
    210928s2019 caua 001 0 eng d
    020
     
     
    a| 9781492041139
    020
     
     
    z| 9781492041108 q| (e-book)
    035
     
     
    a| (HKSYU)b21407381-852hksyu_inst
    040
     
     
    a| MiAaPQ b| eng e| rda e| pn c| MiAaPQ d| MiAaPQ d| HK-SYU
    050
     
    4
    a| QA76.73 b| .G787 D38 2019
    082
    0
     
    a| 005.133 2| 23
    092
    0
     
    a| 005.133 b| GRU 2019
    100
    1
     
    a| Grus, Joel c| (Software engineer), e| author.
    245
    1
    0
    a| Data Science from Scratch : b| First Principles with Python / c| Joel Grus.
    246
    3
    0
    a| First principles with Python
    250
     
     
    a| Second edition.
    264
     
    1
    a| Sebastpol, California : b| O'Reilly, c| [2019]
    264
     
    4
    c| ©2019
    300
     
     
    a| xvii, 384 pages : b| illustrations (black and white) ; c| 24 cm.
    336
     
     
    a| text b| txt 2| rdacontent
    337
     
     
    a| unmediated b| n 2| rdamedia
    338
     
     
    a| volume b| nc 2| rdacarrier
    500
     
     
    a| Includes index.
    520
     
     
    a| To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
    650
     
    0
    a| Python (Computer program language)
    650
     
    0
    a| Database management.
    650
     
    0
    a| Data structures (Computer science)
    907
     
     
    a| b21407381 b| 09-02-22 c| 28-09-21
    910
     
     
    a| ykc b| mkl
    935
     
     
    a| (HK-SYU)501035618 9| ExL
    998
     
     
    a| book b| 09-11-21 c| m d| a e| - f| eng g| cau h| 0 i| 0
    945
     
     
    h| Principal l| location i| barcode y| id f| bookplate a| callnoa b| callnob n| IFYP006