MARC details
| 000 -LEADER |
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| fixed length control field |
03604nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230202s2021 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781801819626 |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
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| ISSN-L |
9781801819626 |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
AUF |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Auffarth, Ben |
| Relator term |
author |
| 9 (RLIN) |
673690 |
| 245 ## - TITLE STATEMENT |
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| Title |
Machine Learning for Time Series with Python Forecast Predict and Detect Anomalies with State of the Art Machine Learning Methods |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Birmingham : |
| Name of publisher, distributor, etc. |
Packt Publishing, |
| Date of publication, distribution, etc. |
c2021 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XV, 352 p. |
| Other physical details |
: ill |
| 490 ## - SERIES STATEMENT |
|---|
| Series statement |
Expert Insight Series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes Bibliographical References and Index |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Get better insights from time-series data and become proficient in model performance analysis<br/>Key Features<br/><br/> Explore popular and modern machine learning methods including the latest online and deep learning algorithms<br/> Learn to increase the accuracy of your predictions by matching the right model with the right problem<br/> Master time series via real-world case studies on operations management, digital marketing, finance, and healthcare<br/><br/>Book Description<br/><br/>The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems.<br/><br/>Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.<br/><br/>This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at real-world case studies covering weather, traffic, biking, and stock market data.<br/><br/>By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.<br/>What you will learn<br/><br/> Understand the main classes of time series and learn how to detect outliers and patterns<br/> Choose the right method to solve time-series problems<br/> Characterize seasonal and correlation patterns through autocorrelation and statistical techniques<br/> Get to grips with time-series data visualization<br/> Understand classical time-series models like ARMA and ARIMA<br/> Implement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning models<br/> Become familiar with many libraries like Prophet, XGboost, and TensorFlow<br/><br/>Who this book is for<br/><br/>This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.<br/> |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Python Computer Program Language |
| 9 (RLIN) |
1561 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
212459 |
| Topical term or geographic name entry element |
Time Series Analysis Data Processing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
845 |
| Topical term or geographic name entry element |
Machine Learning |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Link text |
TOC |
| Uniform Resource Identifier |
<a href="https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9781801819626.pdf">https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9781801819626.pdf</a> |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
WEB LINK |
| Uniform Resource Identifier |
<a href="https://www.oreilly.com/library/view/machine-learning-for/9781801819626/ ">https://www.oreilly.com/library/view/machine-learning-for/9781801819626/ </a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
