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Data science from scratch : first principles with Python / Joel Grus.

By: Material type: TextPublisher: Beijing ; Sebastopol, CA O'Reilly Media, 2019Edition: Second editionDescription: xvii, 384 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781492041139
Subject(s): Genre/Form: DDC classification:
  • 005.7565 GRU 22
Contents:
Introduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Deep learning -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Data ethics -- Go forth and do data science.
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Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
NB - Book (Non borrowing) Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Not for loan 000048876
NB - Book (Non borrowing) Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Not for loan 000048877
Book - Borrowing Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Checked out 16/05/2026 000048878
Book - Borrowing Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Available 000048879
Book - Borrowing Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Checked out 10/10/2023 000048880
Total holds: 0

Includes bibliographical references and index.

Introduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Deep learning -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Data ethics -- Go forth and do data science.

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