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Python for Probability, Statistics, and Machine Learning
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 p...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-30717-6 | ||
| 003 | DE-He213 | ||
| 005 | 20220113004513.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 160316s2016 sz | s |||| 0|eng d | ||
| 020 | |a 9783319307176 |9 978-3-319-30717-6 | ||
| 024 | 7 | |a 10.1007/978-3-319-30717-6 |2 doi | |
| 050 | 4 | |a TK5101-5105.9 | |
| 072 | 7 | |a TJK |2 bicssc | |
| 072 | 7 | |a TEC041000 |2 bisacsh | |
| 072 | 7 | |a TJK |2 thema | |
| 082 | 0 | 4 | |a 621.382 |2 23 |
| 100 | 1 | |a Unpingco, José. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Python for Probability, Statistics, and Machine Learning |h [electronic resource] / |c by José Unpingco. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a XV, 276 p. 110 illus., 7 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation. | |
| 520 | |a 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 | 0 | |a Telecommunication. | |
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 0 | |a Engineering-Data processing. | |
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 0 | |a Data mining. | |
| 650 | 1 | 4 | |a Communications Engineering, Networks. |
| 650 | 2 | 4 | |a Mathematical and Computational Engineering Applications. |
| 650 | 2 | 4 | |a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
| 650 | 2 | 4 | |a Probability and Statistics in Computer Science. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319307152 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319307169 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-30717-6 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||