Now that you know what machine learning is, let’s meet the easiest kind. My goal here is to get humans of all stripes and (almost) all ages comfy with its basic jargon: instance, label, feature, model, algorithm, and supervised learning. You’re dealing with supervised learning if the algorithm has the correct label handy for every instance. Later, it will use the model, or recipe, to label new instances.
When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? Applied data science is a team sport that’s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience. If you’re keen to make your data useful with a decision intelligence engineering approach, here’s my take on the order in which to grow your team.
Data science is the discipline of making data useful. Data science is a ‘concept to unify statistics, data analysis, machine learning and their related methods’ in order to ‘understand and analyze actual phenomena’ with data. When all the facts you need are visible to you, you can use descriptive analytics for making as many decisions as you please. It’s through our actions — our decisions — that we affect the world around us. So it is making data useful.
Machine learning uses patterns in data to label things. Sounds magical? The core concepts are actually embarrassingly simple. How does it actually work? If you were expecting magic, well, the sooner you’re disappointed, the better. Machine learning may be prosaic, but what you can do with it is incredible! It lets helps you write the code you couldn’t come up with yourself, allowing you to automate the ineffable. Don’t hate it for being simple. Levers are simple too, but they can move the world.
You’ve probably heard of machine learning and artificial intelligence, but are you sure you know what they are? If you’re struggling to make sense of them, you’re not alone. There’s a lot of buzz that makes it hard to tell what science is and what’s science fiction. Starting with the names themselves…machine learning is just a thing-labeler, taking your description of something and telling you what label it should get.
Unsupervised learning may sound like a fancy way to say “let the kids learn on their own not to touch the hot oven” but it’s actually a pattern-finding technique for mining inspiration from your data. It has nothing to do with machines running around without adult supervision, forming their own opinions about things. Unsupervised learning helps you find inspiration in data by grouping similar things together for you. There are many different ways of defining similarity, so keep trying algorithms and settings until a cool pattern catches your eye. Let’s demystify!