MARC details
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02698nam a2200289 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
50859 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
0000000000 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240411193249.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230303n s 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319307176 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Unpingco, José. |
| 245 10 - TITLE STATEMENT |
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| Title |
Python for probability, statistics, and machine learning |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc. |
José Unpingco. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Switzerland : |
| Name of publisher, distributor, etc. |
Springer International Publishing, |
| Date of publication, distribution, etc. |
2016. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource. |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Getting Started with Scientific Python Probability Statistics Machine Learning 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 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Communications engineering |
| General subdivision |
telecommunications. |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data mining. |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Maths for computer scientists. |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Maths for engineers. |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Probabilities |
| General subdivision |
Data processing. |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Probability & statistics. |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python (Computer program language) |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Statistics |
| General subdivision |
Data processing. |
| Source of heading or term |
sears |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1XUTT4lxR7N5cTXdk-hRh_w2v-5NjS1KW/view?usp=sharing">https://drive.google.com/file/d/1XUTT4lxR7N5cTXdk-hRh_w2v-5NjS1KW/view?usp=sharing</a> |
