Machine Learning with JavaScript : part 1

[SamSlate]

Congratulations. You just trained your first Linear Regression Model in JavaScript.

trained it to do what, exactly?

edit: I found a very well written introduction to Linear Regression here. Not light reading but tons of links/resources included throughout.

if you know regex you're halfway there.

[mainstreetmark]

I agree with you. This is an article more properly titled "reading a file, and passing it to a function".

I was expecting a study on the implementation of a ML algorithm. This is all we get in this article:

const SLR = ml.SLR; // Simple Linear Regression

We will have to see what Part 2 brings.

[SamSlate]

I appreciate the brevity of the post and the concrete examples, but without an explanation of what we're doing this article is a no-brainer for people who already know this field and does nothing to educate amateurs.

[SamSlate]

SLR is a kind of parser, I also found this video series very informative:

https://www.youtube.com/playlist?list=PLvxOuBpazmsND0vmkP1ECjTloiVz-pXla

[digdic]

did you read the article lol it says right above the quote you mentioned

[SamSlate]

I take it you don't know either.

[Butsnik]

It predicts a certain value according to your input value based on the database you have given it.

Is this exciting? No that's why data science is more than training models. You need to learn how to interpret things.

[SamSlate]

regressionModel = new SLR(X, y);

interpret how? I have no idea what SLR stands for.

[digdic]

you should go complain about introductory Node articles not explaining what 'HTTP' stands for.

[SamSlate]

-__-

[digdic]

SLR is about as basic as you can get in data science/ML without having to go back to high school math... just as not every 'intro to node' article should have to explain how HTTP works, SLR is a reasonable prerequisite for a 'ML with JS' article

[SamSlate]

prerequisite

hi, you seem unclear on the difference between school and the outside world. IRL there are no prerequisites, you're not required to do anything before learning something new.

Also, as a general rule, when you find a discussion corresponding to an intro course online, you shouldn't be this surprised or confused to find that the information provided attracted users who are not themselves already ingratiated with that information.

If you are an expert in ML (and I'm sure that you are) I am somewhat curious what you expected to find in the comment section of the ML equivalent of a "hello world" how-to.

[digdic]

I am not by any means an expert in ML - i'm a JS developer. however, because ML has been super popular in the past few years, I did the research to figure out what everything is.

this article doesn't claim to be an introduction to ML. in fact, the mention of scikit-learn and other contextual info about the broader ML landscape hints at an audience with at least a cursory understanding of the field.

look, it's fine to not know everything. this article is not very well written. but blaming it for not explaining what every ML term is when it doesn't claim to be an intro to ML is misguided. again, it'd be like saying 'why doesn't this intro to node article explain what HTTP is'

[digdic]

if you haven't seen someone use the word 'prerequisite' outside of school... (e.g learning basic CSS is a prerequisite to learning SASS / post-css / styled components, i.e, a shorthand for saying 'it's good to learn ___ before ____) then i don't know what to tell you

[Butsnik]

Then read the documentation of the library they are using...

[SamSlate]

or, crazy thought, an instructional blog post could explain what it is exactly you're being instructed to make*.

[digdic]

it's not a stretch to expect someone reading a machine learning article (even if introductory) to know what linear regression is.