It's often said, "You have to walk before you run" as a staunch position for a "fundamentals first" education. While most AI/ML courses start deep in math, it's arguably the driest choice. We focus on the problem with realistic feedback and interactions.
We'll also focus on practical exercises in a playful way.
We're not afraid of Machine Learning Jargon, but we'll be very careful when we use it. You'll get a JARGON ALERT and we'll break it down together. If I go too fast, you can always pause the video, but for learning benefit, most lectures have a complete text version under each video! These text transcriptions are slightly altered to be completely readable and have all the necessary context, imagery, and code embedded in them - blog post style. This also helps you come back and run a search to find info you might have forgotten for easy reference.
If you've taken our free 5 day mini-course, you'll already know AI or Artificial Intelligence has a plethora of sub-domains. At the top level of AI, there are two major categories. There's AGI (Artificial General Intelligence), which is what everyone thinks of when they mention AI from science fiction. Some people call this "strong AI" or "full AI". This is the underlined plot to countless movies, where AI is basically a problem solving sentient being. Solving "general" intelligence is an ongoing and significant effort. The recent breakthroughs in AI are not AGI breakthroughs. The other domain of AI is ANI (Artificial Narrow Intelligence) which solves specific problems with human or superhuman ability. This can sometimes be referred to as "narrow AI" or "weak AI". This is where things have recently started warming up. The category of ANI includes Machine Learning (ML). In Machine Learning, there are even more divisions upon divisions upon divisions. Supervised, Unsupervised, Reinforcement... where should we start!? We can covert vast domain of ML in a different course. Just as it's not essential to get a degree in art history to start drawing, we can get our hands dirty without memorizing the entire nomenclature. In this course, we'll start with visual feedback. Understand the parts of ML by perception. Machine learning can be applied to text, sound, robots, chemicals, and more, but we're going to focus on image applications as our start. The foundation learned with ML lessons in Computer Vision are some of the most transferable skills to other categories of AI.
We'll also be completely dodging the math. There are countless universities, YouTube videos, and open source repositories that can help you with math. Sure, we could start with graphs, linear algebra, and spreadsheets, but you're not here for that are you? Let's enjoy Machine Learning.