MACHINE LEARNING: A STRUCTURED APPROACH TO ALGORITHMS AND INTELLIGENT SYSTEMS
About This Textbook
This book offers a systematic and in-depth exploration of machine learning, designed to help readers build a strong foundation while progressing toward advanced applications. It begins by introducing the core principles of machine learning, including data representation, statistical thinking, and the fundamental paradigms of supervised, unsupervised, and reinforcement learning.
MACHINE LEARNING: A Structured Approach to Algorithms and Intelligent Systems is an essential textbook designed for university students, researchers, and software engineers. Authored by esteemed computer science educators Dr. Halavath Balaji, Jogu Saritha, and Pallavi B, this comprehensive guide delivers mathematical rigor alongside real-world Python implementation. The textbook thoroughly covers regression models, decision trees, neural networks, deep learning architectures, support vector machines, clustering algorithms, and reinforcement learning framework. Ideal for undergraduate and postgraduate courses in Computer Science, Data Science, and Artificial Intelligence.
Table of Contents
- Chapter 1: Foundations of Machine Learning & Statistical Learning Theory
- Chapter 2: Data Preprocessing, Feature Engineering & Dimensionality Reduction
- Chapter 3: Supervised Learning - Regression & Classification Paradigms
- Chapter 4: Tree-Based Methods, Ensemble Learning & Random Forests
- Chapter 5: Support Vector Machines & Kernel Methods
- Chapter 6: Unsupervised Learning - Clustering & Association Rule Mining
- Chapter 7: Neural Networks, Deep Learning & Optimization Techniques
- Chapter 8: Reinforcement Learning & Intelligent Autonomous Systems
- Chapter 9: Model Evaluation, Validation Metrics & Ethics in AI
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