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Building Machine Learning Systems with Python

ISBN: 9789351102748
Publisher: Shroff Publishers & Distributors Pvt Ltd
Publication Date: 2013-07-01
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  • Regular price $37.99

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Description


As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can 'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data.

Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis. The book also takes you through the latest in computer vision, demonstrating how image processing can be used for pattern recognition, as well as showing you how to get a clearer picture of your data and trends by using dimensionality reduction.

Keep up to speed with one of the most exciting trends to emerge from the world of data science and dig deeper into your data with Python with this unique data science tutorial.

Learn how to create machine learning algorithms using the flexibility of Python Get to grips with scikit-learn and other Python scientific libraries that support machine learning projects Employ computer vision using mahotas for image processing that will help you uncover patterns and trends in your data Learn topic modelling and build a topic model for Wikipedia Analyze Twitter data using sentiment analysis Get to grips with classification and regression with real-world examples

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