This is an online masters-level course on Applied Machine Learning based on Cornell's CS 5785, which provides a comprehensive introduction to both supervised and unsupervised learning. The course covers essential ML algorithms, their mathematical foundations, and practical implementations. Instructors: Volodymyr Kuleshov, Nathan Kallus, Serge Belongie, and Hongjun Wu Course Highlights: - 23 Lectures with detailed course notes - 30+ Hours of lecture videos - 20+ Implementations of ML algorithms in Python Key Topics Covered: - Supervised Learning: Linear Regression, SVMs, Trees, Neural Networks - Unsupervised Learning: Density Estimation, Clustering, Dimensionality Reduction - Evaluation: Model Evaluation, Iterative Model Improvement, Performance Diagnosis Prerequisites: - Programming: Experience in Python recommended but not required (Cornell CS 1110 or equivalent) - Mathematics: Linear algebra (Cornell MATH 2210 or equivalent), Statistics and probability (Cornell STSCI 2100 or equivalent) - No Prior ML Experience Needed: Designed for those new to machine learning Resources Provided: - Video lectures - Lecture slides - Online textbook - Suggested further readings from the ESL textbook