This Machine Learning Foundations course is inspired by the teaching methodology of Andrew Ng and is designed to help beginners understand core machine learning concepts step by step. The course starts with an introduction to machine learning and its real-world applications, followed by a clear explanation of supervised and unsupervised learning.

You will dive deep into linear regression, including training and testing models, understanding cost functions, and optimizing models using the Gradient Descent algorithm. Special focus is given to learning rate (alpha), convergence, and visualization of optimization processes to build strong mathematical intuition.

The course also covers multiple linear regression, working with multiple features, and mastering vectorization techniques to improve computational efficiency. In addition, you will learn how to use Jupyter Notebook and labs for implementing and experimenting with machine learning algorithms in a practical environment.

By the end of this course, you will understand how regression models work mathematically and practically, how optimization improves performance, and how to implement core algorithms efficiently. This course is ideal for beginners, students, and aspiring data scientists seeking a solid ML foundation.