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Synopsis
Machine learning has become an important part of many commercial applications and research projects, from medical diagnostics to finding friends on social networks. Developing machine learning applications does not require large teams of experts: if you bring Python basic knowledge, this practice book will show you how to create your own machine learning solutions.
With Python and the scikit-learn library, you can work out all the steps necessary for a successful machine learning application. Authors Andreas Müller and Sarah Guido focus on the practical aspects rather than the mathematics behind them when using machine learning algorithms. If you are additionally familiar with the NumPy and matplotlib libraries, this will help you get even more out of this tutorial.
The book shows you:
- Basic concepts and applications of machine learning
- Advantages and disadvantages of widely used machine learning algorithms
- how the data processed by Machine Learning can be represented and what aspects of the data you should focus on
- Advanced methods for evaluating models and optimising parameters
- The concept of pipelines used to chain models and encapsulate workflows
- Working methods for text data, especially text-specific processing techniques
- Opportunities to improve your skills in machine learning and data science
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Einführung in Machine Learning mit Python: Praxiswissen Data Science
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