MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02343nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20190824113154.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
190824b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781491901427 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.502855133 |
| Item number |
GRU |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Grus, Joel |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data science from scratch: first principles with Python |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc. |
O'Reilly Media |
| Place of publication, distribution, etc. |
Sebastopol |
| Date of publication, distribution, etc. |
2015 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 311 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
INR |
| Price amount |
800.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Table of Content<br/><br/>Introduction<br/>A crash course in Python<br/>Visualizing data<br/>Linear algebra<br/>Statistics<br/>Probability<br/>Hypothesis and inference<br/>Gradient descent<br/>Getting data<br/>Working with data<br/>Machine learning<br/>k-Nearest neighbors<br/>Naive bayes<br/>Simple linear regression<br/>Multiple regression<br/>Logistic regression<br/>Decision trees<br/>Neural networks<br/>Clustering<br/>Natural language processing<br/>Network analysis<br/>Recommender systems<br/>Databases and SQL<br/>MapReduce<br/>Go forth and do data science. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.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 hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in PythonLearn the basics of linear algebra, statistics, and probability--and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data structures (Computer science) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Database management |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |