Linear and logistic regression, gradient descent, clustering, support vector machines, bias and variance (one of the slides was taken from the course), neural networks, etc...
https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...
It's part of a Machine Learning Specialization on Coursera (5 courses + a capstone project) which goes deeper on some areas after the foundations course: https://www.coursera.org/specializations/machine-learning
I am taking this specialization and I have learned a lot so far. The material seems like it's at exactly the right level of depth (balances giving a high level overview of the field, with enough depth in specific areas to understand how things work and be able to apply them). Disclaimer: I work at Dato, and the CEO of Dato is also one of the instructors of this course.