Hi everyone! I just wrapped up the core theory for Weeks 1, 2, and 3 of Supervised Machine Learning: Regression and Classification. I’ve completed all the videos, slides, and practice quizzes, covering everything from the fundamentals of linear regression and gradient descent to vectorization, feature scaling, and logistic regression for classification.
Now, I am ready to transition from theory to hands-on implementation. My next step is to dive into the Optional and Practice Labs to build out functions like cost calculation and gradient descent from scratch using NumPy. If anyone else is currently working through Course 1 or wants to connect and discuss the vectorization and coding logic, feel free to reach out!