Python for probability, statistics, and machine learning [3rd ed.] (Record no. 567559)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01925 a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250728121947.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250724b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031046506 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.133 |
| Item number | Un6p3 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Unpingco, José |
| 245 ## - TITLE STATEMENT | |
| Title | Python for probability, statistics, and machine learning [3rd ed.] |
| Statement of responsibility, etc | José Unpingco |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 3rd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Springer |
| Year of publication | 2022 |
| Place of publication | Switzerland |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xvii, 509p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers.<br/><br/>Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Python |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Probabilities-data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Statistics-data processing |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Reference |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| On Display | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 24/07/2025 | 2 | 4887.90 | 005.133 Un6p3 | A186930 | 6517.20 | Books |