Introduction to Machine Learning with Python
O'Reilly Media (Verlag)
978-1-4493-6941-5 (ISBN)
With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
Andreas Muller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms. Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.
Erscheint lt. Verlag | 15.11.2016 |
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Verlagsort | Sebastopol |
Sprache | englisch |
Themenwelt | Mathematik / Informatik ► Informatik ► Web / Internet |
ISBN-10 | 1-4493-6941-3 / 1449369413 |
ISBN-13 | 978-1-4493-6941-5 / 9781449369415 |
Zustand | Neuware |
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