This course provides the essential foundations for any beginner who truly wants to master AI and machine learning. Mastering any craft, requires that you have solid foundations. Anyone who is thinking about starting a career in AI and machine learning, will benefit from this. Non-technical professionals such as marketers, business analysts etc. will be able to effectively converse and work with data scientists, machine learning engineers or even data scientists, if they apply themselves to understanding the concepts in this course.

Many misconceptions about artificial intelligence and machine learning are clarified in this course. After completing this course, you will understand the difference between AI, machine learning, deep learning, reinforcement learning, deep reinforcement learning, etc.

The fundamental concepts that govern how machines learn, and the ways in machine-learning use mathematics in the background, are clearly explained. I only reference high school math concepts in this course. This is because neural networks, which are used extensively in all spheres of machine learning, are mathematical function approximators. I therefore cover the basics of functions, and how functions can be approximated, as part of the explanation of neural networks.

If you hate mathematics, then either you will hate this course... or this course might help you to see mathematics differently.

This course does not get into any coding, or complex mathematics. This course is intended to be a baseline steppingstone for more advanced courses in AI and machine learning.