000 -LEADER |
fixed length control field |
a |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
230110b xxu||||| |||| 00| 0 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9781138315068 |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
006.312 |
Item number |
ROG |
100 ## - MAIN ENTRY--PERSONAL NAME |
Personal name |
Rogel-Salazar, Jesus |
245 ## - TITLE STATEMENT |
Title |
Advanced data science and analytics with python |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
Name of publisher, distributor, etc |
CRC Press, |
Date of publication, distribution, etc |
2020 |
Place of publication, distribution, etc |
Boca Raton : |
300 ## - PHYSICAL DESCRIPTION |
Extent |
xxxv, 383 p. ; |
Other physical details |
ill., |
Dimensions |
24 cm |
365 ## - TRADE PRICE |
Price amount |
48.99 |
Price type code |
GBP |
Unit of pricing |
104.60 |
490 ## - SERIES STATEMENT |
Series statement |
Chapman & Hall/CRC data mining & knowledge discovery series |
504 ## - BIBLIOGRAPHY, ETC. NOTE |
Bibliography, etc |
Includes bibliographical references and index. |
520 ## - SUMMARY, ETC. |
Summary, etc |
Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow up from the topics discuss in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered in the book. The book discusses the need to develop data products and tackles the subject of bringing models to their intended audiences. In this case literally to the users's fingertips in the form of an iPhone app. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Database Management |
|
Topical term or geographic name as entry element |
Data mining |
|
Topical term or geographic name as entry element |
Exploration de donnees |
|
Topical term or geographic name as entry element |
Programming and Design |
|
Topical term or geographic name as entry element |
Computer program language |
|
Topical term or geographic name as entry element |
Computer Graphics |
|
Topical term or geographic name as entry element |
Activation function |
|
Topical term or geographic name as entry element |
Bayes' theorem |
|
Topical term or geographic name as entry element |
Chain rule |
|
Topical term or geographic name as entry element |
Backpropogation |
|
Topical term or geographic name as entry element |
LSTM |
|
Topical term or geographic name as entry element |
Hidden layer |
|
Topical term or geographic name as entry element |
Pandas |
|
Topical term or geographic name as entry element |
Social network analysis |
|
Topical term or geographic name as entry element |
Natural language processing |
|
Topical term or geographic name as entry element |
Neural network |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
|
Item type |
Books |