Computer Architecture Coursera

Learn to design the computer architecture of complex modern microprocessors.

Examines key computational abstraction levels below modern high-level languages. From Java/C to assembly programming, to basic processor and system organization. This course examines key computational abstraction levels below modern high-level languages; number representation, assembly language, introduction to C, memory management, the operating-system process model, high-level machine architecture including the memory hierarchy, and how high-level languages are implemented.

Control of Mobile Robots is a course that focuses on the application of modern control theory to the problem of making robots move around in safe and effective ways. The structure of this class is somewhat unusual since it involves many moving parts - to do robotics right, one has to go from basic theory all the way to an actual robot moving around in the real world, which is the challenge we have set out to address through the different pieces in the course.

Design High-Impact User Experiences. Research, design, and prototype effective, visually-driven websites and apps.

Programming-oriented course on effectively using modern computers to solve scientific computing problems arising in the physical/engineering sciences and other fields. Provides an introduction to efficient serial and parallel computing using Fortran 90, OpenMP, MPI, and Python, and software development tools such as version control, Makefiles, and debugging.

The launchpad to a career in IT. This program is designed to take beginner learners to job readiness in under six months.

Launch into the World of Computer Networks. Learn to build and troubleshoot small home networks.

Understand the importance of C and how C, UNIX, and Linux laid the foundation for our entire modern computing environment.

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.

Deep Learning Specialization. Master Deep Learning, and Break into AI