Free Applied Machine Learning Course From Cornell University/Cornell Tech Faculty

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