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

By: Material type: TextTextPublisher: 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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Item type Current library Collection Call number Status Date due Barcode Item holds
NB - Book (Non borrowing) NB - Book (Non borrowing) Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Not for loan 000048876
NB - Book (Non borrowing) NB - Book (Non borrowing) Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Not for loan 000048877
Book - Borrowing Book - Borrowing Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Available 000048878
Book - Borrowing Book - Borrowing Central Library Lower Floor Baccah 005.7565 GRU (Browse shelf(Opens below)) Available 000048879
Book - Borrowing 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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