Details

Autor(en) / Beteiligte

• Triguero, Isaac
• Galar, Mikel

Titel

Large-Scale Data Analytics with Python and Spark : a hands-on guide to implementing machine learning solutions

Ort / Verlag

Cambridge, United Kingdom ; New York, NY ; Port Melbourne, Australia ; New Delhi, India : Cambridge University Press

Erscheinungsjahr

2024

Link zu anderen Inhalten

• Cover

Beschreibungen/Notizen

• Interessenniveau: 4CTM, für die Graduierten- und Postgraduiertenausbildung
• Interessenniveau: 4TC, Lehrbuch, Skript
• Part I. Understanding and Dealing with Big Data: 1. Introduction; 2. MapReduce; Part II. Big Data Frameworks: 3. Hadoop; 4. Spark; 5. Spark SQL and DataFrames; Part III. Machine Learning for Big Data: 6. Machine Learning with Spark; 7. Machine Learning for Big Data; 8. Implementing Classical Methods: k-means and Linear Regression; 9. Advanced Examples: Semi-supervised, Ensembles, Deep Learning Model Deployment.
• Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments