When I first wrote "Introduction to open heart surgery: your housecat" people asked me "why housecats?". Because they are easy to find, and can get you real experience. No need to work up to open heart surgery, it should be available to anyone that has a sharp implement, a housecat, and a thirst for knowledge. Bloody, messy, knowledge.

Because the nature of the subject requires a fair amount of background. To truly understand the subject and a lot of the approaches a firm understand of statistics, data structures, and even some calculus. Usually by the time someone these subjects down enough for anything substantial in ML then they've seen enough different languages to suss out the general idea of most algorithm sample code.

I'm not saying there isn't room for the easier to understand and easier to read guides to ML. More the better, Mitchell was a beast to read through. Its just the language isn't the hard part of the subject. You are the author of the link, correct? I read through some of it, and its approaches the theory and subject matter in a gentle way which is what matters. The sample code is easy to read. I've written maybe 100 lines of js in my life and avoid all web dev like the plague. Your guide is well written and useful. I am not dogging it at all and please don't take it that way. I think its great!

What I'm saying is if someone is saying to themselves "I would be able to learn machine learning if only their was a guide in X" then they are probably mistaken. The code is easy, the math and theory is what is hard.

> Its just the language isn't the hard part of the subject.

For you, sure -- but not for everyone.

This series has actually been up for a little over a year now. I get emails from people who didn't know what machine learning was before they started reading the articles, and now they're building some of the most creative and beautiful projects out there. I also get emails from people who need to implement ML in JS or C-like languages but have had trouble seeing the algorithms in full relief when translating from Python, for instance.

The point is, your experience is not everyone's experience. My goal is purely one of accessibility of education. There are smart, talented people who never played with ML simply because they didn't want to dive into a different language, different platform, and different environment just to muck around. There are people who hadn't heard of ML before, but tried it out because JS was right there for them. There are people who stayed away from ML because they thought higher math and a CS education were requirements. Those are facts. This series serves all those people, and it serves them well.

I just want to make sure that its clear that I think you are hitting that goal and your posts look great. There is nothing worse than posting something on the internet to have some snarky neckbeard from the peanut gallery put it down for some tangential reason.

>"To truly understand the subject and a lot of the approaches a firm understand of statistics, data structures, and even some calculus. Usually by the time someone these subjects down enough for anything substantial in ML then they've seen enough different languages to suss out the general idea of most algorithm sample code."

I learned calculus and linear algebra long before learning to code.

I don't mean to be snarky, but how was your data structures knowledge then?

He asked "Is there some requirement that ML _must_ be an advanced topic" and I listed a few prerequisite pieces of knowledge that make it fairly advanced. You may learn those prerequisites in a different order but they are required before you properly tackle machine learning without cargoculting through it.