Implement Machine Learning and Deep Learning Techniques with Python /
First Statement of Responsibility
Akshay R. Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V. Adithya Krishnan.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
New York, NY :
Name of Publisher, Distributor, etc.
Apress L. P.,
Date of Publication, Distribution, etc.
[2023]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
174p.
Other Physical Details
illustrations.
NOTES PERTAINING TO BINDING AND AVAILABILITY
Text of Note
Available to OhioLINK libraries.
CONTENTS NOTE
Text of Note
Chapter 1: Getting Started with Time Series -- Chapter 2: Statistical Univariate Modelling -- Chapter 3: Statistical Multivariate Modelling -- Chapter 4: Machine Learning Regression-Based Forecasting -- Chapter 5: Forecasting Using Deep Learning.
0
SUMMARY OR ABSTRACT
Text of Note
This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
O'Reilly Media
Stock Number
9781484289785
OTHER EDITION IN ANOTHER MEDIUM
Author
Kulkarni, Akshay R.
Place of Publication
Berkeley, CA : Apress L. P.,c2023
Title
Time Series Algorithms Recipes
International Standard Book Number
9781484289778.
TOPICAL NAME USED AS SUBJECT
Entry Element
Time-series analysis
Entry Element
Time-series analysis
Entry Element
Machine learning
Entry Element
Python (Computer program language)
Topical Subdivision
Computer programs.
Topical Subdivision
Data processing.
Topical Subdivision
Computer programs.
(SUBJECT CATEGORY (Provisional
Subject Category Subdivision Code
UYQM
Subject Category Subdivision Code
COM004000
Subject Category Subdivision Code
UYQM
System Code
bicssc
System Code
bisacsh
System Code
thema
DEWEY DECIMAL CLASSIFICATION
Number
006
.
31
Edition
23/eng/20230105
LIBRARY OF CONGRESS CLASSIFICATION
Class number
HA30
.
3
Book number
.
K85
2023
PERSONAL NAME - PRIMARY RESPONSIBILITY
Entry Element
Kulkarni, Akshay R.,
PERSONAL NAME - SECONDARY RESPONSIBILITY
Entry Element
Shivananda, Adarsha,
Entry Element
Kulkarni, Anoosh,
Entry Element
Krishnan, V. Adithya,
CORPORATE BODY NAME - SECONDARY RESPONSIBILITY
Entry Element
Ohio Library and Information Network.
ORIGINATING SOURCE
Agency
کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه
Date of Transaction
20231218061624.0
Cataloguing Rules (Descriptive Conventions))
rda
ELECTRONIC LOCATION AND ACCESS
Electronic name
Implement Machine Learning and Deep Learning Techniques with Python-Apress (20.pdf