Software and Robotics Courses that I want to Follow This Year

Every once in a while, I came across with a great course, but I keep forgetting them later. In this post, I want to share the courses I am planning to follow this year. Realistically, I won’t be able to finish all of them in one year, but still this list might be useful for you too :)

(I will keep editing this post with more cool resources)

• C++ For Yourself: Sensor fusion and localization engineer Igor Bogoslavskyi explains modern C++ in this course with practical assignments.
• fast.ai’s Deep Learning Course: This course by Jeremy Howard is a great complement to Coursera’s Deep Learning Specialization since this course explains more about the practical applications with examples.
• Introduction to Robotics by Princeton University: Finally, I found a modern introduction to robotics course which is available online for free and not from ages ago. Huge thanks to course intructor Anirudha Majumdar.
• Welcome to Basic Knowledge on Visual SLAM: From Theory to Practice, by Xiang Gao, Tao Zhang, Qinrui Yan and Yi Liu: Finding a SLAM course online is quite difficult. I want to explore this book to see how much it covers the topic. I hope I can find some practical examples too.
• Modern Robotics: Mechanics, Planning, and Control Coursera Specialization: If I magically happen to have more time, I wanna dive into this course to gain a deeper understanding of robotics theory.
• Book - Foundation of Robotics: A Multidisciplinary Approach with Python and ROS: This book seems like a easy read. I am planing to skim it to make sure that I do not have a gap in my knowlodge about robotics fundementals.
• Coursera - First Principles of Computer Vision Specialization by Colombia University: This coursera specialization seems to be a great way to learn the theory behind the general computer vision.
• [Udacity – Become a Sensor Fusion Engineer Nanodegree Program]: This nanodegree program is more tailored towards obstacle detection, especially for robotics applications. It explains how to fuse LIDAR, RADAR and camera data with Kalman filters.