Start with “Why?” and end with “I’m ready!”
If your understanding of A.I. and Machine Learning is a big question mark, then this is the blog post for you. Here, I gradually increase your Awesomenessicity™ by gluing inspirational videos together with friendly text.
Sit down and relax. These videos take time, and if they don’t inspire you to continue to the next section, fair enough.
However, if you find yourself at the bottom of this article, you’ve earned your well-rounded knowledge and passion for this new world. Where you go from there is up to you.
Understanding Why Machine Learning is so HOT Right Now
A.I. was always cool, from moving a paddle in Pong to lighting you up with combos in Street Fighter.
A.I. has always revolved around a programmer’s functional guess at how something should behave. Fun, but programmers aren’t always gifted in programming A.I. as we often see. Just Google “epic game fails” to see glitches in A.I., physics, and sometimes even experienced human players.
Regardless, A.I. has a new talent. You can teach a computer to play video games, understand language, and even how to identify people or things. This tip-of-the-iceberg new skill comes from an old concept that only recently got the processing power to exist outside of theory.
I’m talking about Machine Learning.
You don’t need to come up with advanced algorithms anymore. You just have to teach a computer to come up with its own advanced algorithm.
So how does something like that even work? An algorithm isn’t really written as much as it is sort of… bred. I’m not using breeding as an analogy. Watch this short video, which gives excellent commentary and animations to the high-level concept of creating the A.I.
Wow! Right? That’s a crazy process!
Now how is it that we can’t even understand the algorithm when it’s done? One great visual was when the A.I. was written to beat Mario games. As a human, we all understand how to play a side-scroller, but identifying the predictive strategy of the resulting A.I. is insane.
Impressed? There’s something amazing about this idea, right? The only problem is we don’t know Machine Learning, and we don’t know how to hook it up to video games.
Fortunately for you, Elon Musk already provided a non-profit company to do the latter. Yes, in a dozen lines of code you can hook up any A.I. you want to countless games/tasks!
Why Should You Use Machine Learning?
I have two good answers on why you should care. Firstly, Machine Learning (ML) is making computers do things that we’ve never made computers do before. If you want to do something new, not just new to you, but to the world, you can do it with ML.
Secondly, if you don’t influence the world, the world will influence you.
Right now significant companies are investing in ML, and we’re already seeing it change the world. Thought-leaders are warning that we can’t let this new age of algorithms exist outside of the public eye. Imagine if a few corporate monoliths controlled the Internet. If we don’t take up arms, the science won’t be ours. I think Christian Heilmann said it best in his talk on ML.
“We can hope that others use this power only for good. I — for one, don’t consider this a good bet. I’d rather play and be part of this revolution. And so can you.”
OK, now I’m interested…
The concept is useful and cool. We understand it at a high level, but what the heck is actually happening? How does this work?
If you want to jump straight in, I suggest you skip this section and move on to the next “How Do I Get Started” section. If you’re motivated to be a DOer in ML, you won’t need these videos.
If you’re still trying to grasp how this could even be a thing, the following video is perfect for walking you through the logic, using the classic ML problem of handwriting.
Pretty cool huh? That video shows that each layer gets simpler rather than more complicated. Like the function is chewing data into smaller pieces that end in an abstract concept. You can get your hands dirty in interacting with this process on this site (by Adam Harley).
It’s cool watching data go through a trained model, but you can even watch your neural network get trained.
One of the classic real-world examples of Machine Learning in action is the iris data set from 1936. In a presentation I attended by JavaFXpert’s overview on Machine Learning, I learned how you can use his tool to visualize the adjustment and back propagation of weights to neurons on a neural network. You get to watch it train the neural model!
Even if you’re not a Java buff, the presentation Jim gives on all things Machine Learning is a pretty cool 1.5+ hour introduction into ML concepts, which includes more info on many of the examples above.
These concepts are exciting! Are you ready to be the Einstein of this new era? Breakthroughs are happening every day, so get started now.
How do I get started?
There are tons of resources available. I’ll be recommending two approaches.
Nuts n Bolts
In this approach, you’ll understand Machine Learning down to the algorithms and the math. I know this way sounds tough, but how cool would it be to really get into the details and code this stuff from scratch!
If you want to be a force in ML, and hold your own in deep conversations, then this is the route for you.
I recommend that you try out Brilliant.org’s app (always great for any science lover) and take the Artificial Neural Network course. This course has no time limits and helps you learn ML while killing time in line on your phone.
This one costs money after Level 1.
Combine the above with simultaneous enrollment in Andrew Ng’s Stanford course on “Machine Learning in 11 weeks”. This is the course that Jim Weaver recommended in his video above. I’ve also had this course independently suggested to me by Jen Looper.
Everyone provides a caveat that this course is tough. For some of you that’s a show stopper, but for others, that’s why you’re going to put yourself through it and collect a certificate saying you did.
This course is 100% free. You only have to pay for a certificate if you want one.
With those two courses, you’ll have a LOT of work to do. Everyone should be impressed if you make it through because that’s not simple.
But more so, if you do make it through, you’ll have a deep understanding of the implementation of Machine Learning that will catapult you into successfully applying it in new and world-changing ways.
If you’re not interested in writing the algorithms, but you want to use them to create the next breathtaking website/app, you should jump into TensorFlow and the crash course.
If taking a course is not your style, you’re still in luck. You don’t have to learn the nitty-gritty of ML in order to use it today. You can efficiently utilize ML as a service in many ways with tech giants who have trained models ready.
I would still caution you that there’s no guarantee that your data is safe or even yours, but the offerings of services for ML are quite attractive!
Using an ML service might be the best solution for you if you’re excited and able to upload your data to Amazon/Microsoft/Google. I like to think of these services as a gateway drug to advanced ML. Either way, it’s good to get started now.
Let’s Be Creators
I have to say thank you to all the aforementioned people and videos. They were my inspiration to get started, and though I’m still a newb in the ML world, I’m happy to light the path for others as we embrace this awe-inspiring age we find ourselves in.
It’s imperative to reach out and connect with people if you take up learning this craft. Without friendly faces, answers, and sounding boards, anything can be hard. Just being able to ask and get a response is a game changer. Add me, and add the people mentioned above. Friendly people with friendly advice helps!
Hey #machinelearning friends like @sugargreenbean and @jenlooper Do you often find high variance solutions are a common ML issue? Or is this like learning shell sort? Where it’s good to know but not use?
I hope this article has inspired you and those around you to learn ML!
Have a minute? Check out a few more of my posts:
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