Publish Book Chapter
Data Mining
Invited Topics
Introduction: Understanding the Foundations of Machine Learning
Data Input: Concepts, Instances, and Attributes
Output Representation: Structuring Learned Knowledge
- Core Algorithms: Fundamental Learning Techniques
Model Evaluation: Assessing Reliability and Performance
Data Preparation: Preprocessing and Exploratory Analysis
Ms. K. Tamilselvi, Assistant Professor, Artificial Intelligence and Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi, Ms. G. Umamaheswari, Assistant Professor, Artificial Intelligence and Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi, Ms. E. Jananandhini, Assistant Professor, Computer Science and Engineering, P. A. College of Engineering and Technology, Pollachi, Ms. E. Gayathri, Assistant Professor, Cyber Security, Dr. Mahalingam College of Engineering and Technology, Pollachi- Ethical Considerations: Impacts and Responsibilities in Learning Systems
Ensemble Techniques: Combining Models for Better Performance
- Model Extensions: Enhancing Instance-Based and Linear Approaches
Deep Learning Foundations: Principles and Architectures
Ms. M. Ramya, Mr. Manoj, Ms. R. Pradeepa, Assistant Professors, Department of Information Technology, PPG Institute of technology, CoimbatoreBeyond Traditional Paradigms: Reinforcement and Semi-Supervised Learning
Introduction to Probabilistic Learning: Fundamental Concepts
Advanced Probabilistic Models and Inference Methods
Applications and Implications: Real-World Use and Future Directions
Book Scope
- This edited book offers a comprehensive exploration of machine learning, guiding readers from foundational concepts to advanced techniques. It covers essential topics such as data representation, input attributes, and output knowledge structures, followed by an in-depth discussion of core learning algorithms. Emphasis is placed on data preprocessing and exploratory analysis to ensure high-quality inputs, while model evaluation techniques are detailed to assess credibility and performance. The book also examines ethical considerations, highlighting the societal impact of machine learning applications. Ensemble learning, instance-based extensions, and deep learning fundamentals are explored to illustrate both traditional and modern approaches. Further chapters delve into probabilistic methods, including both foundational and advanced models, as well as learning paradigms beyond supervised and unsupervised techniques. Real-world applications and their implications are discussed, reinforcing the practical relevance of machine learning. Designed for students, researchers, and practitioners alike, this book serves as a valuable resource for understanding and applying machine learning effectively across diverse domains.
Handling Editor
Handling editor name
Description about the handling editor
Author Guidelines
How To Submit
OR
Email your book chapter at info@techdiscoveryhubpublisher.in
Customer helpline number: +919791944316
Deadline
20.06.2025