I want to be a machine learning engineer. That's my goal. And so when a buddy told me about a practical, hands-on, not-too-theoretical-or-math-heavy machine learning course that taught you the critical things you need to get started and be effective at the job, I was interested.

That course was the Zero to Mastery Complete Machine Learning and Data Science course, which can be found here: https://zerotomastery.io/courses/machine-learning-and-data-science-bootcamp/

I'm not an affiliate or anything, but I can recommend it. I'm a little over halfway through the course and have already learned tons about the tools that are used in the day-to-day of actual machine learning engineers.

But what exactly is machine learning? It's a question I didn't realize I didn't know the answer to until I started the course. I had in my head a vague idea of robots, of Jarvis from Iron Man, of the future.

Simply, machine learning finds patterns in data (if there are any to be found), and then applies those patterns to new data to make predictions.

So for example, let's say you're a hospital with a ton of patient data stored on a spreadsheet. The spreadsheet data contains things like resting heart rate, chest pain, blood pressure, age, sex, cholesterol, blood sugar, and so on, as well as if the patient ended up being diagnosed with heart disease. You want to be able to predict if a new patient who comes is likely to have heart disease. That's where machine learning comes in.

You see, for a human staring at that huge database full of complicated numbers, it would be quite difficult to extrapolate any meaning from it. You might be able to work out a couple of very general trends, but you're going to be limited. Human brains simply don't work like that. Computers, or machines, on the other hand, work quite well with those spreadsheets full of numbers. If you use an appropriate algorithm, the machine can find patterns in the data that would be undetectable by humans.

And so the hospital, in possession of an accurate model, could take the data of a new patient coming in and predict, with reasonable accuracy, whether or not the patient had heart disease.

That's machine learning in a nutshell.

It can be applied to all sorts of problems in the modern world and there are lots of different algorithms to choose from depending on the type of problem you're facing.

I'm thankful for this course opening my eyes and allowing me to get hands-on with actually using real models on some real data and seeing the real predictions. When I look back at where I was just a month ago, it seems I've already leveled up tons.

Of course, there's mountains more to learn, and that's ok. My plan is to continue learning for the rest of my life, so I have all the time I need.