Machine Learning with Python for Everyone
Auflage 1
Machine Learning with Python for Everyone
Auflage 1
Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.
Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.
Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.
- Understand machine learning algorithms, models, and core machine learning concepts
- Classify examples with classifiers, and quantify examples with regressors
- Realistically assess performance of machine learning systems
- Use feature engineering to smooth rough data into useful forms
- Chain multiple components into one system and tune its performance
- Apply machine learning techniques to images and text
- Connect the core concepts to neural networks and graphical models
- Leverage the Python scikit-learn library and other powerful tools
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- eBook (Kortext PDF) 39,58 €
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Produktdetails
Verlagsnummer: 9780134845647
ISBN: 978-0-13-484564-7
Produkttyp: eBook (Kortext ePub)
Verlag: Pearson International
Erscheinungsdatum: 30.07.2019
Dateigröße in MB: 53.83
Auflage: 1
Sprache: Englisch
Artikelbeschreibung
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python
Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.
Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.
Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.
Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.
- Understand machine learning algorithms, models, and core machine learning concepts
- Classify examples with classifiers, and quantify examples with regressors
- Realistically assess performance of machine learning systems
- Use feature engineering to smooth rough data into useful forms
- Chain multiple components into one system and tune its performance
- Apply machine learning techniques to images and text
- Connect the core concepts to neural networks and graphical models
- Leverage the Python scikit-learn library and other powerful tools