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Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

Język publikacji: 1
Autor:
Sinan Ozdemir
Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition Sinan Ozdemir - okladka książki

Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition Sinan Ozdemir - okladka książki

Serie wydawnicze:
Hands-on
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
326
Dostępne formaty:
     PDF
     ePub

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Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.
Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.
With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.
By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.
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O autorze książki

Sinan Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at the Johns Hopkins University. He then spent several years conducting lectures on data science there, before founding his own start-up, Kylie ai, which uses artificial intelligence to clone brand personalities and automate customer service communications. He is also the author of Principles of Data Science, available through Packt.

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