A complete course about Machine and Deep learning; the fundaments of AI.
What you will learn
Get acquainted with useful ML math, such as statistics, probability theory and linear algebra.
Learn to make ML algorithms from scratch.
Learn how the “back-end” of ML works.
Learn from zero the pipeline of using ML to solve a problem.
Learn to work with deep-learning frameworks (PyTorch)
Learn how to visualize and interpret correctly the given data, as well as the output of an algorithm.
Why take this course?
Welcome everyone! My name is Anastasis and I am an Electrical and Computers Engineer, finishing my studies at Aristotle University of Thessaloniki. My diploma thesis is on Vision Language Models, and based on that I decided that many more students could be introduced in the world of Machine Learning, as I have been, and become inspired to make research using this very powerful tool known as AI models. This course is a “demo” to the bigger course that will soon be released. It includes only the first part of a whole lot of many chapters of Machine Learning that we are going to explore and thoroughly understand together. This course’s main objective is to help provide the students a very clear and in depth understanding of how Machine Learning algorithms work, in order to become an adept data engineer or scientist! Students are more than encouraged to leave feedback, as it is the only way to help me become able to help you better understand a complex topic, and make the teaching process as pleasant as possible! By this course’s end, I hope that I will have inspired the student that completed it to delve into the great depths of the vast subject of Machine Learning, as I believe that knowledge of this subject is a “mandatory” skill for today’s computer scientists and engineers.