Linear Regression: Basics and Gradient Descent

This lecture introduces the basics of linear regression, covering topics such as feature engineering, logistic regression, and ethics in AI. It explains the definition of machine learning, the ingredients of ML, and the types of learning experiences. The lecture delves into supervised vs. unsupervised learning, using examples like Iris Unlabeled and Palmer Penguins 2. It discusses the difference between supervised and unsupervised datasets, showcasing the Palmer Penguins 2 dataset. The lecture also explores the concepts of regression versus classification, illustrating them with examples like predicting house prices in Portland. Additionally, it explains the process of minimizing the cost function using techniques like the normal equation and gradient descent.