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Artificial Intelligence and Machine Learning [#1011583]
Artificial Intelligence and Machine Learning
by Dr. Balakrishnan G., Prof. Jenifer J., Mrs. Subbulakshmi R.
English | 2026 | ASIN: B0GL992476 | 121 Pages | PDF | 75 MB
Artificial Intelligence (AI) and Machine Learning (ML) represent one of the most transformative technological advancements of the modern era. These fields aim to develop computer systems capable of performing tasks that normally require human intelligence, such as learning from experience, recognizing patterns, understanding language, making decisions, and solving complex problems. Today, AI and ML influence nearly every aspect of daily life, from smart assistants and recommendation systems to medical diagnosis, financial forecasting, autonomous vehicles, and scientific research.
Artificial Intelligence is a broad discipline that focuses on creating intelligent agents-systems that can perceive their environment, reason logically, and act rationally to achieve specific goals. Machine Learning, a core subset of AI, enables systems to learn from data without being explicitly programmed. Instead of following rigid instructions, ML algorithms improve their performance over time by identifying patterns and relationships within data.
This subject, Artificial Intelligence and Machine Learning, is designed to introduce students to the fundamental concepts, theories, and practical techniques that drive intelligent systems. The course begins with the foundations of AI, including problem-solving strategies, knowledge representation, search algorithms, and reasoning methods. These topics provide insight into how machines simulate intelligent behavior and make optimal decisions.
The Machine Learning component focuses on data-driven approaches to intelligence. Students are introduced to various learning paradigms such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Core algorithms including linear regression, decision trees, k-nearest neighbors, support vector machines, clustering techniques, and neural networks are explained with clarity and practical relevance. The course also covers performance evaluation, model validation, and overfitting, which are critical for building reliable ML models.
Special emphasis is placed on real-world applications of AI and ML. Case studies and examples from healthcare, bioinformatics, agriculture, finance, robotics, natural language processing, and computer vision help students understand how theoretical concepts are applied in practice. Ethical considerations, fairness, transparency, and the social impact of AI are also discussed to encourage responsible and informed use of intelligent technologies.
This book adopts a balanced approach between theory and application. Mathematical foundations are presented in an intuitive manner, supported by examples, diagrams, and simplified explanations. Where appropriate, algorithmic workflows and pseudo-code are included to enhance conceptual understanding. The content is structured progressively, allowing learners from diverse academic backgrounds to build confidence and competence in AI and ML.
The material is intended for undergraduate and postgraduate students in computer science, engineering, data science, life sciences, and related disciplines. It also serves as a foundation for advanced studies in deep learning, data analytics, robotics, and intelligent systems research.
By the end of this course, learners will develop the ability to understand, design, and evaluate intelligent systems. More importantly, they will gain the analytical mindset required to solve complex problems using data and computational intelligence. This subject empowers students to actively participate in the rapidly evolving world of artificial intelligence and to contribute meaningfully to innovation and societal progress.
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