Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.
I decided to write a post I’ve been wishing existed for a long time. A simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone.
Whether you are a programmer or a manager.
Let’s roll.
Classical Machine Learning
The first methods came from pure statistics in the ’50s. They solved formal math tasks — searching for patterns in numbers, evaluating the proximity of data points, and calculating vectors’ directions.
Nowadays, half of the Internet is working on these algorithms. When you see a list of articles to “read next” or your bank blocks your card at random gas station in the middle of nowhere, most likely it’s the
work of one of those little guys.
Big tech companies are huge fans of neural networks. Obviously. For them, 2% accuracy is an additional 2 billion in revenue. But when you are small, it doesn’t make sense. I heard stories of the teams spending
a year on a new recommendation algorithm for their e-commerce website, before discovering that 99% of traffic came from search engines. Their algorithms were useless. Most users didn’t even open the main page.
Despite the popularity, classical approaches are so natural that you could easily explain them to a toddler. They are like basic arithmetic — we use it every day, without even thinking.