What is Machine Learning?
Machine learning, algorithms, artificial intelligence…these are all words you will come across as you train to be a data scientist. If you don’t have a background in computer science, coding, math, statistics, or anything from the sciences you may have only come across these words in your periphery or on the news. Now, I *like* math and coding, but I do not have a degree in them and, frankly, the way some of the concepts are described is always sterile and sometimes confusing.
In an effort to spread the love on data science, I’m going to try to tackle some of the most common concepts and keywords and re-explain them in my own simple words, without all the boring jargon. Join me.
So…what is machine learning?
To keep it short and sweet: machine learning is a bit of code with some math included (as in, there is code that does the math for you) that someone has written on a computer. Some historical data is then processed through this code and it spits out a prediction or decision or the like, depending on what question you were looking to answer.
The data is always historical because, well, I don’t think anyone can time-travel to the future yet? The precise code and math will also vary depending on the programming language (code) used and what type of question it is.
There are three different types of machine learning (I’m sure some will argue otherwise):
Note that I am intentionally not calling them by their “proper” names.
- one where you provide the “machine” (code written on a computer…) with data that already has the answers with the goal that when you give it new data without the answers that it will provide the correct answers
- another is when you do not provide the answers, just the data, and you want the “machine” to group things in some logical way, that logic depends on the code
- and the third is one I have never used so I may trip up here…here the “machine” can take data with answers plus it learns from new data coming in and makes the best decision that way. It’s what is used for self-driving cars, and when you play chess against a computer…I think
And there you have it. Machine learning in the simplest terms! The tech crowd will know I’ve skipped over some big keywords such as algorithms, but fear not, this is just the beginning.
If you enjoyed this and found it helpful (or totally unhelpful) please let me know by leaving a reaction or comment, or find me via jasminludolf.com.