Python Machine Learning : Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
Language: English
Published by Packt Publishing, 2015
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 74.03
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsKey FeaturesBook DescriptionMachine 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.Python 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.What you will learnWho this book is forIf 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.…
Seller Inventory # 9781783555130
- Title
- Python Machine Learning : Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
- Author
- Sebastian Raschka
- Publisher
- Packt Publishing
- Publication year
- 2015
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1783555130
- ISBN 13
- 9781783555130
- Item weight
- 841 grams
- Dimensions
- 235x191x25 mm
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
Key Features
Book Description
Machine 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.
Python 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.
What you will learn
Who this book is for
If 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.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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