Machine Learning with Python IBM Full Course | Python Machine Learning Basic | Scikit Learn

Machine Learning with Python is a comprehensive IBM full course that guides learners through the entire machine learning lifecycle. Beginning with an introduction at 00:00:00, the course covers an overview of machine learning at 00:02:46, the model lifecycle at 00:10:46, and a day in the life of a machine learning engineer at 00:12:48. It contrasts data scientists and AI engineers at 00:20:28, explores tools at 00:30:58, and delves into the scikit‑learn ecosystem at 00:39:32. The curriculum then moves into regression techniques, starting with an introduction at 00:44:36, simple linear regression at 00:48:59, multiple linear regression at 00:54:06, and polynomial and non‑linear regression at 01:01:42. Logistic regression is introduced at 01:08:55, followed by training a logistic regression model at 01:15:28. Classification topics include decision trees at 01:27:34, regression trees at 01:34:38, support vector machines at 01:40:42, and k‑nearest neighbors at 01:47:42. Ensemble methods and bias‑variance trade‑offs are covered at 01:54:08. Clustering strategies begin at 02:00:35, with k‑means at 02:07:54, DBSCAN and HDBSCAN at 02:15:21, and dimensionality reduction and feature engineering at 02:21:57. The course then discusses dimension reduction algorithms at 02:26:37, classification metrics at 02:31:19, regression metrics at 02:37:40, and evaluation of unsupervised learning models at 02:43:10. Advanced validation techniques are presented at 02:50:23, regularization at 02:56:08, and data leakage at 03:03:13.