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Sinopsis
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you:
- Reference real-world examples to test each algorithm through engaging, hands-on exercises
- Apply test-driven development (TDD) to write and run tests before you start coding
- Explore techniques for improving your machine-learning models with data extraction and feature development
- Watch out for the risks of machine learning, such as underfitting or overfitting data
- Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Matthew Kirk has always been "the math guy" to those that know him best. He started his career as a quantitative financial analyst with Parametric Portfolio. While there, he studied momentum and reversal effects in Emerging Markets and optimized their 30 billion dollar portfolio.
He left the finance industry to build the current version of Wetpaint.com, an entertainment website that is visited by over 10 million unique visitors each month. One of his accomplishments while there was the initial prototype of their patent pending Social Publishing Platform, which optimizes their publication strategy for Facebook posting.
He left Wetpaint to work with a small startup in Kansas City called SocialVolt as their Chief Scientist. While there, he worked on sentiment analysis tools and spam filtering of social media data.
In 2012 he started Modulus 7, which is a data science and startup consulting firm. His clients have included Ritani, The Clymb, Siren, Sqoop, and many others.
Matthew holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He is also studying for his M.S. in Computer Science at the Georgia Institute of Technology.
He has spoken around the world about using machine learning and data science with Ruby. When he's not working, he enjoys listening to his 2000+ vinyl record collection on his Thorens TD160 Mk2 turntable.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Thoughtful Machine Learning with Python
Publicado por
O'Reilly Media, 2017
ISBN 10: 1491924136
ISBN 13: 9781491924136
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Paperback. Condición: Very Good. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms. Nº de ref. del artículo: 00102275826
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Thoughtful Machine Learning with Python: A Test-Driven Approach
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O'Reilly Media, 2017
ISBN 10: 1491924136
ISBN 13: 9781491924136
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Thoughtful Machine Learning with Python
Publicado por
O'Reilly Media, 2017
ISBN 10: 1491924136
ISBN 13: 9781491924136
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Librería: World of Books Inc, Montgomery, IL, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Paperback. Condición: Very Good. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms. Nº de ref. del artículo: CIN1491924136VG
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ISBN 13: 9781491924136
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