What is machine learning?
Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It’s the same algorithm but it’s fed different training data so it comes up with different classification logic.
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Thanks to machine learning, there’s never been a more exciting time in the history of computer science. Every day, new breakthroughs are changing what’s possible with computers.
You might be intimidated by machine learning or think it’s something that only the top companies and research institutions can use, but that’s not true. Machine Learning is for everyone—and it’s fun! Machine Learning is Fun is a valuable, introductory blog. It covers the tenets of ML through interactive tutorials and practical examples, which make it easier to see the useful applications to different businesses and industries. Author Adam Geitgey is a former software developer who now consults organizations on implementing machine learning. He believes ML is integral to the future of software and that developers should have a strong working knowledge, so he provides guides and techniques to help them develop and grow.
Two kinds of Machine Learning Algorithms
You can think of machine learning algorithms as falling into one of two main categories — supervised learning and unsupervised learning. The difference is simple, but really important.
Supervised Learning
Let’s say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee agents to help you out. But there’s a problem — you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don’t have your experience so they don’t know how to price their houses.
To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on it’s size, neighborhood, etc, and what similar houses have sold for.
So you write down every time someone sells a house in your city for 3 months. For each house, you write down a bunch of details — number of bedrooms, size in square feet, neighborhood, etc. But most importantly, you write down the final sale price:
