MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02589nam a22002537a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ISI Library, Kolkata |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251129020017.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250428b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789352138326 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
ISI Library |
| Language of cataloging |
English |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23rd |
| Classification number |
005.7 |
| Item number |
G892 |
| 100 10 - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Grus, Joel |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Data science from scratch: |
| Remainder of title |
first principles with Python/ |
| Statement of responsibility, etc |
Joel Grus |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Beijing: |
| Name of publisher, distributor, etc |
O'Reilly, |
| Date of publication, distribution, etc |
2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 384 p. : |
| Other physical details |
graphs, illustration; |
| Dimensions |
23 cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes index |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Introduction -- 2. A crash course in python -- 3. Visualizing data -- 4. Linear algebra -- 5. Statistics -- 6. Probability -- 7. Hypothesis and inference -- 8. Gradient descent -- 9. Getting data -- 10. Working with data -- 11. Machine learning -- 12. k-Nearest neighbors -- 13. Naive Bayes -- 14. Simple linear regression -- 15. Multiple regression -- 16. Logistic regression -- 17. Decision trees -- 18. Neural networks -- 19. Deep learning -- 20. Clustering -- 21. Natural language processing -- 22. Network analysis -- 23. Recommender systems -- 24. Databases and SQL -- 25. MapReduce -- 26. Data ethics -- 27. Go forth and do data science -- Index |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.<br/><br/>If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.<br/><br/>Get a crash course in Python<br/>Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science<br/>Collect, explore, clean, munge, and manipulate data<br/>Dive into the fundamentals of machine learning<br/>Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering<br/>Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer Science |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data Science |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
| Koha issues (borrowed), all copies |
4 |
