Unlocking the Secrets of Data: A Comprehensive Guide to Principles and Theory for Data Mining and Machine Learning

In the era of big data and artificial intelligence, data mining and machine learning have become indispensable tools for businesses, researchers, and individuals. To effectively harness the power of these technologies, a deep understanding of their underlying principles and theory is crucial.

"Principles and Theory for Data Mining and Machine Learning" by Springer International Publishing is a comprehensive textbook that provides a rigorous and in-depth exploration of the fundamental concepts and algorithms in data mining and machine learning. Written by a team of leading experts, this book is an essential resource for students, practitioners, and researchers in these fields.

• Comprehensive Coverage: Covers the full spectrum of data mining and machine learning topics, from data preprocessing to model evaluation.
• Rigorous Mathematical Foundation: Provides a solid theoretical basis for understanding the underlying principles and algorithms.
• Practical Examples and Case Studies: Illustrates the concepts and techniques with real-world examples and case studies.
• Exercises and Solutions: Includes numerous exercises and solutions to enhance understanding and reinforce concepts.
• Instructor Resources: Provides instructors with lecture slides, solutions to exercises, and additional teaching materials.

• Undergraduate and graduate students in data mining, machine learning, and related fields
• Professionals working in data science, machine learning, and artificial intelligence
• Researchers and academics seeking a comprehensive understanding of data mining and machine learning theory

• Chapter 1:
• Chapter 2: Data Preprocessing
• Chapter 3: Exploratory Data Analysis
• Chapter 4: Probability and Statistics

• Chapter 5: Classification
• Chapter 6: Regression
• Chapter 7: Ensemble Methods

• Chapter 8: Clustering
• Chapter 9: Dimensionality Reduction
• Chapter 10: Association Rule Mining

• Chapter 11: Hypothesis Testing and Model Selection
• Chapter 12: Computational Complexity
• Chapter 13: Optimization

• Chapter 14: Applications in Business
• Chapter 15: Applications in Healthcare
• Chapter 16: Applications in Social Sciences

• Gain a comprehensive understanding of the principles and theory underlying data mining and machine learning.
• Develop the analytical skills necessary to solve real-world data problems.
• Enhance your ability to design and implement effective data mining and machine learning algorithms.
• Prepare for a successful career in data science, machine learning, or artificial intelligence.

• Michael Steinbach: Professor of Computer Science at the University of Minnesota, Minneapolis, USA
• Hans-Peter Kriegel: Professor of Computer Science at the Ludwig Maximilian University of Munich, Germany
• Sanjay Kumar: Associate Professor of Computer Science at the University of North Carolina at Charlotte, USA

"This textbook is a valuable resource for students and professionals alike. It provides a comprehensive and rigorous treatment of the fundamental concepts and theory in data mining and machine learning." - Professor Jiawei Han, University of Illinois at Urbana-Champaign

Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)

by John R. Erickson

5 out of 5

Language	:	English
File size	:	19169 KB
Screen Reader	:	Supported
Print length	:	798 pages
Paperback	:	41 pages
Item Weight	:	5.8 ounces
Dimensions	:	8.5 x 0.1 x 11 inches
X-Ray for textbooks	:	Enabled

FREE

"The authors have done an excellent job of presenting the material in a clear and accessible manner. The book is well-suited for both undergraduate and graduate courses, as well as for self-study." - Professor Alex Smola, Our Book Library

Free Download "Principles and Theory for Data Mining and Machine Learning" today from Springer International Publishing: https://link.springer.com/book/9783319983005

Unlock the world of data and unlock your potential with this essential guide.

Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)

by John R. Erickson

5 out of 5

Language	:	English
File size	:	19169 KB
Screen Reader	:	Supported
Print length	:	798 pages
Paperback	:	41 pages
Item Weight	:	5.8 ounces
Dimensions	:	8.5 x 0.1 x 11 inches
X-Ray for textbooks	:	Enabled

FREE