Python: Deeper Insights into Machine Learning

4.8 (12 reviews total)
By John Hearty , Sebastian Raschka , David Julian
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About this book

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.

The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.

The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems.

At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.

Publication date:
August 2016
Publisher
Packt
Pages
901
ISBN
9781787128576

About the Authors

  • John Hearty

    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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  • Sebastian Raschka

    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.

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  • David Julian

    David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was a technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.

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Latest Reviews

(12 reviews total)
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Python: Deeper Insights into Machine Learning
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