In today’s data-driven world, organizations increasingly depend on intelligent systems to analyze information, automate decisions, and generate meaningful insights. Machine Learning one of the most influential fields of artificial intelligence enables computers to learn patterns from data and improve performance through experience. With the rapid growth of digital information, Machine Learning has become an essential technology empowering innovation in business, healthcare, education, finance, and numerous other domains.
This course, “Machine Learning,” provides a comprehensive and balanced understanding of the key principles, learning models, and algorithms that form the foundation of modern intelligent systems. It introduces learners to the fundamental concepts of how machines acquire knowledge, represent hypotheses, and generalize from examples. The book explores diverse learning paradigms including symbolic learning, neural networks, evolutionary models, probabilistic approaches, instance-based learning, and advanced rule-based systems.
Further, the course emphasizes the importance of both theoretical understanding and practical application. Students gain insight into how learning algorithms work internally, how models are trained and optimized, and how learning systems can be used to solve real-world problems. Ethical considerations, such as model fairness, interpretability, and responsible use of data, are also highlighted as essential components of modern Machine Learning practice.
The primary aim of this subject is to equip learners with strong analytical skills and conceptual clarity, enabling them to apply Machine Learning techniques confidently and effectively. By the end of the course, students will be well-prepared to pursue advanced studies or professional careers in artificial intelligence, data science, business analytics, and related fields where intelligent decision-making plays a central role.
Contents –
UNIT 1 Basics
UNIT 2 Neural Networig and Genetic Algorithms
UNIT 3 Bayesian and Computational Learning
UNIT 4 Instance Based Learning
UNIT 5 Advanced Learning
