Thinking Data Science: A Data Science Practitioner's Guide Sarang Poornachandra
Thinking Data Science: A Data Science Practitioner's Guide Sarang Poornachandra This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist…
Specifikacia Thinking Data Science: A Data Science Practitioner's Guide Sarang Poornachandra
Thinking Data Science: A Data Science Practitioner's Guide Sarang Poornachandra
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? Can I rely on AutoML for model development? Should I use GOFAI, ANN/DNN or Transfer Learning?
How do I handle high-frequency dynamic datasets? What if the client provides me Gig and Terabytes of data for developing analytic models? 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.
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