How to Learn Machine Learning as a Designer –

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The Best Free Resources to Learn from in a Non-Technical Manner

As a designer its crucial to understand the technology you’re working with in order to know its limitations and capabilities. Smashing Magazine agrees with me on this. The tricky thing is that most resources for learning new technologies are made by technicians for technicians. That leaves designers clueless and jumping on the train of knowledge when it’s already departing.

Therefore I collected the best resources for designers to learn a currently important technology: Machine Learning. All of the resources I selected, approach this topic more intuitively than a typical articles wrote for developers. Nevertheless, you will require a bit of technical thinking and knowledge to get the most out of it.

Let’s dive into the list:

Artificial Intelligence

If you only have basic (or science-fiction) knowledge about AI, you should start by gaining a deeper understanding this field to fully grasp machine learning later on.

This one will give you a pretty good overview of the current state and dig a bit into the technologies below whilst presenting it in a beautiful manner:

AI Overview | Snips
A 6 minute Intro to

A good resource to see what can be done with Artificial intelligence apart from dystopical imaginations and self-driving cars is Google AI Experiments. It hooks you up with a whole bunch of different topics and even lets you play with the experiments. This is a artificial intelligence guessing what you draw:

Quick, Draw!
Can a neural network learn to recognize doodles? See how well it does with your drawings and help teach it, just by…

Machine Learning

As you gained (or had) profound knowledge about AI, we’re getting to the point were the real magic happens. You should know, without machine learning and it’s newly-gained speed, modern AI wouldn’t be possible.

As most designers are so called “visual-learners” this page does an incredible job, of visualizing the concepts and basics of machine learning:

A visual introduction to machine learning
Let's revisit the 73-m elevation boundary proposed previously to see how we can improve upon our intuition. Clearly…

If you prefer watching videos to study, here’s a Udacity picodegree about Machine Learning which is great, although it gets more technical the longer you watch.

Since this gentle introduction follows up with some deeper tutorials about supervised vs. unsupervised learning, it’s definitely worth reading (and bookmarking). Might be a bit technical for some, though.

A Gentle Introduction to Machine Learning — The New Stack
This piece is the latest in a series, called “Machine Learning Is Not Magic,” covering how to get started in machine…

As soon as you learned about the general concept of machine learning and know the difference between unsupervised and supervised, it’s time to learn about the specific algorithms used, so you can do jargon talk with developers (just kidding). Here’s a good overview for that:

Machine Learning Explained: Algorithms Are Your Friend
We hear the term "machine learning" a lot these days, usually in the context of predictive analysis and artificial…

Deep Learning & Neural Networks

With Deep Learning & Neural Networks we’re getting our hands dirty and dig deep into the specifics of this field. So if you only understand the general concepts, please don’t worry, this is advanced stuff.

This video is the best one I could find to explain this topic in a human manner and even refreshes your knowledge of Machine Learning (if you’re starting at this point for whatever reason). It’s once again from Udacity and is even more technical than the first one, probably due to the difficulty of the topic.

All other material I encountered is heavily understandable (at least in my opinion) and everybody who wants to master this, should be ready for a lot of theory.

Further Reading

If you want to get a feeling about the complexity AI can reach and how the AI “brain” might work, take a look at one of Google’s experiments which is visualizing high-dimensional space.

This article is actually a big list itself with a whole bunch of resources to get news about AI:

The Non-Technical Guide to Machine Learning & Artificial Intelligence

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Author: Florian Oefner

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