This article was written by Adam Geitgey. Adam is interested in computers and machine learning. He likes to write about it.
This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is.
The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.
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.
This machine learning algorithm is a black box that can be re-used for lots of different classification problems.
“Machine learning” is an umbrella term covering lots of these kinds of generic algorithms.
What you will find in this article:
- Two kinds of Machine Learning Algorithms: Supervised Learning, Unsupervised Learning
- That’s cool, but does being able to estimate the price of a house really count as “learning”?
- Let’s write that program!
- Mind Blowage Time
- What about that whole “try every number” bit in Step 3?
- What else did you conveniently skip over?
- Is machine learning magic?
- How to learn more about Machine Learning
To check out all this information, click here. For more articles about Machine Learning, click here.
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