Machine Learning Algorithms and Applications

This course provides a comprehensive learning in the field of machine learning, covering fundamental, advanced concepts, techniques, and applications. The course will guide students through the basics of machine learning algorithms, data preprocessing, model evaluation, and deployment. Students can learn the differences between supervised, unsupervised, and reinforcement learning, and how they are applied in real-world scenarios. Awareness of key machine learning algorithms, including linear regression, clustering, support vector machines, and mixture models, is provided. In depth knowledge on the role of probability in classification, regression, and clustering and the various mathematical functions behind them is discussed in detail. The various aspects of improving model performance and how to evaluate models using various metrics and optimize their performance are explained. Students can discover a wide range of machine learning applications using the knowledge gained over the course. This course is ideal for students, professionals, and anyone interested in entering the field of machine learning. No prior experience in machine learning is required, but familiarity with programming and basic math concepts will be beneficial. All concepts are explained with real time examples, and problems are solved to understand the applications in the real world. More content will be added in the future to go with a deep dive into machine learning.