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Thinking Data Science - A Data Science Practitioner's Guide (Hardcover, 1st ed. 2023)
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Thinking Data Science - A Data Science Practitioner's Guide (Hardcover, 1st ed. 2023)
Series: The Springer Series in Applied Machine Learning
Expected to ship within 9 - 15 working days
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This 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.
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