Machine Learning: Hands-On for Developers and Technical Professionals - Softcover

Bell, Jason

Softcover

ISBN 10: 1118889061 ISBN 13: 9781118889060

Verlag: Wiley, 2014

Inhaltsangabe

Dig deep into the data with a hands-on guide to machine learning

Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.

At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:

• Learn the languages of machine learning including Hadoop, Mahout, and Weka
• Understand decision trees, Bayesian networks, and artificial neural networks
• Implement Association Rule, Real Time, and Batch learning
• Develop a strategic plan for safe, effective, and efficient machine learning

By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.

Über diesen Titel

Über die Autorin bzw. den Autor

Jason Bell has been working with point of sale and customer loyalty data since 2002 and has been involved in software development for more than 25 years. He works as a senior technical architect, lecturer and also advises startups that are just beginning their technical adventures.

Von der hinteren Coverseite

Go deep data diving with this hands-on guide to machine learning

If you want to get into machine learning but fear the math, this book is your ultimate guide. Specifically designed for non-mathematicians, this useful guide presents a breakdown of each variant of machine learning, with examples and working code. You&;ll learn the various algorithms, data preparation techniques, trees, and networks, and get acquainted with the tools that help you get more from your data. You&;ll understand how it works, where it&;s used, and how to make it great.

• Learn the languages of machine learning: Weka, Mahout^&;, Spark^&;, and R
• Make the right data storage and cleaning decisions, tailored to your desired output
• Understand decision trees, Bayesian networks, artificial neural networks, and association rule learning
• Implement support vector machines knowing the relevant advantages and limitations
• Apply Big Data processing techniques with Hadoop^®, Mahout, and MapReduce
• Use Spring XD to capture streaming data and learn in real time
• Access the tools you need to plan your project and acquire and process data
• Study examples and use provided working code for hands-on learning

Details zur Ausgabe

• Verlag: Wiley
• Erscheinungsdatum: 2014
• Sprache: Englisch
• ISBN 10: 1118889061
• ISBN 13: 9781118889060
• Einband: Tapa blanda
• Anzahl der Seiten: 408
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Nicht verfügbar