Python for probability, statistics, and machine learning (Record no. 103194)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02176nam a22001937a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 978-3319307152 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.133 UNP |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Unpingco, Jose |
| 245 ## - TITLE STATEMENT | |
| Title | Python for probability, statistics, and machine learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | USA |
| Name of publisher, distributor, etc. | Springer |
| Date of publication, distribution, etc. | 2016 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xiv, 276 p. |
| Other physical details | 24 cm ; Hard |
| 500 ## - GENERAL NOTE | |
| General note | Alpha/2434/ Rs.7399/- |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Getting Started with Scientific Python --<br/>Probability --<br/>Statistics --<br/>Machine Learning --<br/>Notation. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Probabilities--Data processing |
| -- | Python (Computer program language) |
| -- | Statistics--Data processing |
| -- | Mathematical statistics |
| -- | Data mining |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Unpingco, Jose |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://www.springer.com/in/book/9783319307152">http://www.springer.com/in/book/9783319307152</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Total Renewals | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GSB Collection | 17/06/2017 | 3 | 5 | 005.133 UNP | B2401 | 13/07/2021 | 25/06/2019 | 17/06/2017 | Books |