Machine Learning Full Course with Scikit‑Learn | Math + Python + AI | Sagar Chouksey

In this 4‑hour comprehensive machine learning tutorial, you’ll learn everything needed to master machine learning, AI fundamentals, and Scikit‑Learn, starting from the mathematical foundations and progressing to real‑world coding. The course covers the essential math for machine learning, the foundations of AI, ML, and DL, core machine learning topics such as regression, classification, KNN, and decision trees, data preprocessing techniques, supervised learning, model evaluation metrics, and concludes with a full student‑score prediction project that walks through the entire pipeline from data to deployment. Timestamps: 5:29 – 44:04 → Maths Required for Machine Learning 44:04 – 1:05:41 → Foundation of Machine Learning (AI vs ML vs DL, Concepts) 1:05:41 – 1:14:57 → Core Machine Learning Topics (X/y, Models, Predictions) 1:14:57 – 2:05:11 → Data Preprocessing (Missing Data, Encoding, Scaling, Splitting) 2:05:11 – 2:59:19 → Supervised Machine Learning (Regression, Classification, KNN, Decision Trees) 2:59:19 – 3:34:00 → Model Evaluation & Metrics (Accuracy, Precision, Recall, F1, MAE, MSE, RMSE) 3:34:00 – 4:13:19 → Student Score Prediction Project (Start-to-Finish Walkthrough)