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Advanced Machine Learning with Python: Solve data science problems by mastering cutting-edge machine learning techniques in Python

De (autor): John Hearty

Advanced Machine Learning with Python: Solve data science problems by mastering cutting-edge machine learning techniques in Python - John Hearty

Advanced Machine Learning with Python: Solve data science problems by mastering cutting-edge machine learning techniques in Python

De (autor): John Hearty

Hearty, John: - John Hearty is a Manager of Data Science team with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually, John struck out on his own as a consultant offering a comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favorite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfill a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation
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Hearty, John: - John Hearty is a Manager of Data Science team with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually, John struck out on his own as a consultant offering a comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favorite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfill a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation
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