This course provides a basic introduction to the field of machine learning (ML) and deep learning (DL). Students will gain a solid understanding of the fundamental concepts, algorithms, and techniques used in ML and DL. The course will cover both supervised and unsupervised learning paradigms, exploring topics such as linear regression, logistic regression, neural networks, and clustering.

Course Objectives:

Develop a strong foundation in the principles and methodologies of ML and DL.

Learn how to implement and apply ML and DL algorithms to real-world problems.

Understand the ethical implications of ML and DL, including bias and fairness.

Gain hands-on experience with ML and DL tools and frameworks.

Develop the ability to critically evaluate and analyze ML and DL models.