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VOOZH | about |
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VOOZH | about |
Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.
Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs.
Any technology user today has benefitted from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of friends. Optical character recognition (OCR) technology converts images of text into movable type. Recommendation engines, powered by machine learning, suggest what movies or television shows to watch next based on user preferences. Self-driving cars that rely on machine learning to navigate may soon be available to consumers.
Machine learning is a continuously developing field. Because of this, there are some considerations to keep in mind as you work with machine learning methodologies, or analyze the impact of machine learning processes.
In this tutorial, we’ll look into the common machine learning methods of supervised and unsupervised learning, and common algorithmic approaches in machine learning, including the k-nearest neighbor algorithm, decision tree learning, and deep learning. We’ll explore which programming languages are most used in machine learning, providing you with some of the positive and negative attributes of each. Additionally, we’ll discuss biases that are perpetuated by machine learning algorithms, and consider what can be kept in mind to prevent these biases when building algorithms.
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Community and Developer Education expert. Former Senior Manager, Community at DigitalOcean. Focused on topics including Ubuntu 22.04, Ubuntu 20.04, Python, Django, and more.
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thanks a lot for the article!
Thanks Lisa for the article!
Does a digital ocean Linux server have the Dlib library preinstalled? If not, does it support Dlib?
Hey! Thanks for such good article! Really useful :)
Well, Lisa, this is a fabulous topic and though your writing is exemplary, you somehow lost me somewhere between paragraph 6 and 9 (not a difficult thing to do apparently).
I struggled on for a few more paragraphs but had to give it up. Guess, maybe my last 3 girlfriends were right, perhaps I really only do read ‘pictures’, lol
Anyway, quite stupidly, I decided that maybe you put the pictures at the end. So, I rapidly started reading from the last paragraph coming backwards, ostensibly looking for the pictures.
Ha ha, that eased the boredom and I quickly got back to the same paragraph where i got bogged down like molasses - but alas, no pictures …
So, now I see that according to indeed.com (2016 ,Dec ) Python, Java, R and c++ are amongst the most used ML languages, perhaps you could redo this article when you have time in the future and sprinkle it with quicky examples that we noobies can quickly throw up in a droplet and really try to dive into in this exciting field in five plus minutes. Yeah, you can see my ADD talking here.
If it can’t do something with incredible speed and brilliance in 5 minutes, it thinks its probably not worth doing, lol.
In truth, perhaps you could actually put those same examples directly into the Machine Learning AI image, so that starting the machine instance will give us some of those same examples to quickly look at.
And yes, I booted up a droplet to see what it ( the empty Jupyter Notebook) would looked like and for the noobies amongst us ( which includes me ) it’s like looking at a blank page with nothing on it.
Edit: There are pictures here. Have no idea how I missed them the first time thru.
Awesome article, I’m willing to learn about Machine Learning and this is a great start point to know what this is about. Thanks!
Cool) and when is the beginning?
Hi Lisa, I must say that this is very informative blog. I am also working on some applications that use machine learning algorithm. I would like to add as well.
Following are some of the easy-to-understand & publicly used examples of machine learning & AI- -Chat bots -Voice recognition and responsive system. The above two, learn to refine the response time to time. The older these system gets the better are the responses.
Perfect for beginners as for me)
Hey, that’s a great content!! Thanks for sharing your knowledge. If you are up to this kind of resources, you may find this machine learning tutorial interesting: http://bit.ly/2BHHeYB - I’d appreciate your feedback!
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