Machine Learning for Everybody – Full Course

This beginner‑friendly course covers the fundamentals of machine learning, starting with an introduction at 0:00:00, followed by a data and Colab introduction at 0:00:58, and an introduction to machine learning at 0:08:45. It then discusses features at 0:12:26, explores classification and regression techniques at 0:17:23, and covers training models at 0:19:57. Data preparation is addressed at 0:30:57, followed by K‑Nearest Neighbors at 0:44:43 and its implementation at 0:52:42. The course continues with Naive Bayes at 1:08:43 and its implementation at 1:17:30, logistic regression at 1:19:22 and its implementation at 1:27:56, support vector machines at 1:29:13 and their implementation at 1:37:54, neural networks at 1:39:44, TensorFlow at 1:47:57, classification neural networks using TensorFlow at 1:49:50, linear regression at 2:10:12, its implementation at 2:34:54, linear regression using a neuron at 2:57:44, regression neural networks using TensorFlow at 3:00:15, K‑Means clustering at 3:13:13, principal component analysis at 3:23:46, and K‑Means and PCA implementations at 3:33:54.