000			02977nam a22003375i 4500
001			21744360
003			OSt
005			20260306144541.0
008			201005s2020 inu 000 0 eng
010			_a 2020947403
020			_a9789363866249
035			_a21744360
040			_aDLC _beng _erda _cDLC
042			_apcc
082			_21 _a006.31 _bLAZF
100	1		_aLazzeri, Francesca. _9254051
245	1	0	_aMachine Learning : _bfor Time Series Forecasting With Python / _cFrancesca Lazzeri.
250			_a1st ed.
263			_a2011
264		1	_aNew Delhi : _bWiley India Pvt Ltd. _c2025.
300			_axxii,230 p.; _bPB _c23.5 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aMachine Learning for Time Series Forecasting with Python is full real-world examples, resources, and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. This book is perfect for entry-level data scientists, business analysts, developers, and researchers. This book offers a comprehensive introduction to the core concepts, terminology, approaches, and applications of machine learning and deep learning for time series forecasting: understanding these principles leads to more flexible and successful time series applications. Apart from this, in this Indian Adaptation, you’ll get: ·New self-evaluation Multiple Choice, Review, and Job-Interview Questions to equip the knowledge and confidence to excel in interviews ·India-specific case studies, Time Series Forecasting for Demand Planning at Flipkart and Predictive Maintenance at Tata Steel. ·Appendix on “Enhancing Time Series Forecasting for the Indian Market”
505			_rMachine Learning for Time Series Forecasting with Python is full real-world examples, resources, and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. This book is perfect for entry-level data scientists, business analysts, developers, and researchers. This book offers a comprehensive introduction to the core concepts, terminology, approaches, and applications of machine learning and deep learning for time series forecasting: understanding these principles leads to more flexible and successful time series applications. Apart from this, in this Indian Adaptation, you’ll get: ·New self-evaluation Multiple Choice, Review, and Job-Interview Questions to equip the knowledge and confidence to excel in interviews ·India-specific case studies, Time Series Forecasting for Demand Planning at Flipkart and Predictive Maintenance at Tata Steel. ·Appendix on “Enhancing Time Series Forecasting for the Indian Market”
650			_aTime series data preparation _9254085
650			_2Introduction to neural networks for time series forecasting
942			_2ddc _cBK _e1 _k006.31 LAZF
999			_c240849 _d240849