This Machine Learning course is designed to provide a comprehensive understanding of the development and application of algorithms that enable computers to perform tasks without explicit instructions. Students will learn about various types of machine learning, including supervised, unsupervised, and reinforcement learning, and delve into advanced topics such as neural networks, deep learning, and ensemble methods. The course covers practical applications in natural language processing and computer vision, ensuring that students can apply their knowledge to real-world scenarios. Additionally, the course addresses ethical considerations in machine learning, emphasizing the importance of fairness, transparency, and privacy. By the end of the course, students will be equipped with the skills and knowledge to develop, evaluate, and deploy machine learning models effectively, and will be prepared for future advancements in the field.

(Information Course - Non-Certification Course)