Hacker News Reader

Comment by bsbechtel | Hacker News Reader

I'm waiting for a "Machine Learning Tools for the Non-technical" post. When we can make machine learning accessible to business owners and non-technical people, this is when machine learning will really have a big impact on society. Until then, it's accessibility is just too limited.

View on HN

Im currently trying to crack it. What I've realised so far is that once you get past the scary mathematic notation, its really quite simple under the hood. Maybe that wont be the case as I learn more, but linear regression and gradient descent is really very simple and easy to grok.

So far I've been cobblibg together a learning regimen by combing Coursera, Khan Academy, and a book called "Machine Learning for Programmers". Individually they all eventually brush over some key concepts that make it dofficult to get an intuition for what's happening. But when combined they fill in the gaps. Also, this repo has been like the rosetta stone for someone who doesn't have a single maths qualification, not even a GCSE.

https://github.com/Jam3/math-as-code/blob/master/README.md

I'm in a similar boat, and that repo looks very useful! Thanks.

If you're at all into Python or R I'd suggest Jose Portilla's courses on Udemy as well, they're very good at covering the applied aspects.

This has generally been my experience with mathematic notation applied to CS topics; the notation itself is basically impenetrable, but once you can get the underlying ideas translated into readable form they turn out to be straightforward and easy to apply.

I hope you are aware that those introductory courses give you a rather high level view of things and often hide any problematic points from you. It's like driving and only learning to know about the accelerator.

For practical use cases, machine learning is actually easier than what these blog posts imply. One of my pet peeves with some marketing thought pieces on Machine Learning/AI is that they deliberately abstract so much of the process in order to sound smarter. (the original submission did a good job in explaining the terminology/code, however)

ML workflows can nowadays be simplified to a few lines of code, and is one of the reasons I now open-source my code in Jupyter/R Notebooks to help facilitate transparency.

The accessibility of machine learning does not affect its impact on society. It already has had a big impact on society whether people realize it or not. It is used for countless tasks that people take for granted (spam filter, facial recognition, translation, etc.).

We live in a society where people are completely dependent on technologies that they know nothing about. The average person who uses a computer or a smartphone wouldn't even be able to tell you how to represent the number 2 in binary yet these devices have a huge impact on their lives. Whether this is a good thing or a bad thing I'm not sure since it is no longer possible to understand every aspect of technology at our current (and accelerating) level of progress.

> The accessibility of [cars] does not affect its impact on society.

I would think the accessibility of something is directly related to its impact on society most of the time. There is no doubt ml has already had an impact, but putting it in the hands of non-technical users opens up possibilities that the technical crowd is incapable of imagining.

I'm not sure I understand your 'cars' comment. The vast majority of people who drive cars have no idea how an engine works yet they are completely dependent on them.

'How' something works doesn't affect it's impact on society, the more important consideration is 'what' it does.

My original point was that machine learning is not something that the average person can apply in his or her own business yet. Yes, big tech companies use it to improve their operations, but a small business owner can't define what would even be an applicable ML problem for their business yet. Millions of small businesses have websites, and even their own custom software, yet ML is still out of reach for many of these business. This is where I think a huge impact can, but has yet, to be made.

I completely agree with you. It just seemed like your initial comment was dismissing the effect/impact of ML because not everyone has the time to learn how to implement it.

Machine Learning only works if you have data that it can use. Non-technical people are unlikely to be in possession of this data. But I still agree with you.

This is starting to get what I'm trying to express....non-technical people don't even know what ML is capable of, how to define what could be a ML problem, or how to go about acquiring the tools to solve said problem. Once that information is easily made available to at the very least the average programmer, then I think ML will have a much larger impact than we can imagine.

I think nutonian.com had cracked that(machine learning for non-programmers) quite well.

Reply on news.ycombinator.com