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- 454 Seiten
- 16 Lesestunden
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Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi... Machine learning drives many groundbreaking technologies today, enabling us to convert abundant data into actionable knowledge. From automated speech recognition on smartphones to email spam filters and movie recommendation systems, machine learning is integral to modern applications. The rise of powerful open-source libraries has made machine learning accessible to all, with Python serving as an ideal platform for developing these systems efficiently. This resource will guide you through the fundamentals of machine learning and its practical applications using Python. You will learn step-by-step best practices for transforming raw data into valuable insights, efficiently developing learning algorithms, and evaluating outcomes. The content covers various problem categories that machine learning addresses, including object classification, regression analysis for predicting continuous outcomes, and clustering to uncover hidden data structures. Additionally, you will create a sentiment analysis machine learning system and learn how to integrate your model into a web application to share with others.
Buchkauf
Python Machine Learning, Sebastian Raschka, Randal S. Olson
- Sprache
- Erscheinungsdatum
- 2015
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- Titel
- Python Machine Learning
- Sprache
- Englisch
- Autor*innen
- Sebastian Raschka, Randal S. Olson
- Verlag
- Packt Publishing
- Erscheinungsdatum
- 2015
- Einband
- Paperback
- Seitenzahl
- 454
- ISBN10
- 1783555130
- ISBN13
- 9781783555130
- Reihe
- Schlagwörter
- Sachbücher, Technologie & Industrie, Computer & Internet, Handbücher und Anleitungen, Technologie, Künstliche Intelligenz
- Bewertung
- 4,25 von 5 Sternen
- Beschreibung
- Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi... Machine learning drives many groundbreaking technologies today, enabling us to convert abundant data into actionable knowledge. From automated speech recognition on smartphones to email spam filters and movie recommendation systems, machine learning is integral to modern applications. The rise of powerful open-source libraries has made machine learning accessible to all, with Python serving as an ideal platform for developing these systems efficiently. This resource will guide you through the fundamentals of machine learning and its practical applications using Python. You will learn step-by-step best practices for transforming raw data into valuable insights, efficiently developing learning algorithms, and evaluating outcomes. The content covers various problem categories that machine learning addresses, including object classification, regression analysis for predicting continuous outcomes, and clustering to uncover hidden data structures. Additionally, you will create a sentiment analysis machine learning system and learn how to integrate your model into a web application to share with others.