Über den Autor
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.
Klappentext
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsnKey Featuresnn Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationnn Learn effective strategies and best practices to improve and optimize machine learning systems and algorithmsnn Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasetsnnnnnnBook DeillegalscriptionnnMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.nnPython Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.nnWhat you will learnnnn Explore how to use different machine learning models to ask different questions of your datann Learn how to build neural networks using Keras and Theanonn Find out how to write clean and elegant Python code that will optimize the strength of your algorithmsnn Discover how to embed your machine learning model in a web application for increased accessibilitynn Predict continuous target outcomes using regression analysisnn Uncover hidden patterns and structures in data with clusteringnn Organize data using effective pre-processing techniquesnn Get to grips with sentiment analysis to delve deeper into textual and social media datannnnnnWho this book is fornnIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
