000			02321nam a22002895i 4500
001			91920
005			20231110140212.0
010			_a978-3-031-02363-7 _dcompra
090			_a91920
100			_a20231023d2023 k||y0pory50 ba
101			_aeng
102			_aCH
200			_aThinking data science _bDocumento eletrónico _ea data science practitioner's guide _fby Poornachandra Sarang
210			_aCham _cSpringer _d2023
215			_aXX, 358 p. _cil.
225			_aThe Springer Series in Applied Machine Learning
303			_aThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single "Cheat Sheet". The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.
606			_aMachine learning
606			_aArtificial intelligence _xData processing
606			_aArtificial intelligence
680			_aQ325.5-.7
700			_aSarang _bPoornachandra
801			_aPT _gRPC
856			_uhttps://doi.org/10.1007/978-3-031-02363-7
942			_2lcc _cF _n0