Machine Learning Algorithms Explained: AI/ML Engineer Guides

Alright listen, so I’m gonna be honest with you. When I first started working with machine learning algorithms at HostGet, I had no clue what I was doing. Like zero. My team was throwing these terms at me – supervised learning, neural networks, decision trees – and I’m sitting there nodding like I understand but I’m completely lost inside my head.

I’m Likhon Hussain. I run operations at HostGet, and before this I was just a cloud engineer trying to figure things out. Now I work with AI and machine learning every single day. And you know what? It’s way simpler than people make it sound. I’m gonna break this down for you the way I wish someone had broken it down for me.

So What Even Is Machine Learning?

Look, forget all that Wikipedia definition stuff. Here’s what machine learning actually is: you take a bunch of data, you show it to a computer, and the computer learns patterns from that data. Then later when you give it new data, it can make predictions or decisions based on what it learned. That’s it. That’s the whole thing.

It’s like if you showed someone a thousand pictures of dogs and cats. After a while, when you show them a new picture, they can tell you what animal it is. The computer does the same thing. Now there’s basically two ways this works, and this is important so pay attention.

Two Different Ways to Do Machine Learning

Supervised Learning – When You Know The Answer

Supervised learning is when you’re teaching something. You’ve got data and you already know what the right answer should be for each piece of data. Like remember when I said the dog and cat thing? That’s supervised learning. You’re showing the computer what’s a dog and what’s a cat, so it learns.

At work, we use this all the time. We have historical data about cloud server costs, right? We know that when someone uses this much processing power, it costs this much money. So we train an algorithm on that data. Now when a new customer asks us what their cost will be, the algorithm can predict it pretty accurately.

Or think about spam emails. Gmail has millions of emails that are definitely spam and millions that definitely aren’t spam. They train their system on that. So when a new email comes to your inbox, the system looks at it and goes “yep, this looks like spam” or “nope, this is legit.”

The point is – you know the answers upfront. You’re teaching by example.

Within supervised learning there’s two things:

Regression – you’re guessing a number. How much will this cost? How many people will sign up next month? What temperature will it be? Numbers.

Classification – you’re picking a category. Spam or not spam? High risk or low risk? Cat or dog? You’re putting things into buckets.

I see people mix these up constantly and use the wrong approach for their problem. It’s like trying to use a screwdriver to hammer a nail. Might look similar but it doesn’t work.

Unsupervised Learning – Figure Out The Pattern Yourself

Unsupervised learning is the opposite. You’ve got data but you DON’T know what the answer should be. You’re basically telling the computer “go figure out what’s interesting about this data.”

Like if you dumped a thousand pictures of animals on your desk and said “organize these however makes sense to you” without telling anyone what cats or dogs are. The computer would look at them and go “okay these ones look similar to each other, those ones look similar to each other.”

We use this when we want to understand our data better. Maybe I want to group customers into different types without having a predefined idea of what those types are. Or look at server logs and find weird patterns without knowing what I’m looking for. That’s unsupervised learning.

The Actual Algorithms – Let Me Tell You About The Ones That Matter

Linear Regression – The Foundation

Okay so linear regression. This is the one everyone starts with and honestly it’s boring but super important. Here’s what it does. Imagine you have a bunch of dots on a paper. Some are high, some are low, and they kind of follow a pattern. Linear regression just draws a line through all those dots as close as possible.

Why would you want to do that? Because if you have that line, you can predict where new dots would go. I did this once for predicting cloud storage costs. We had data – we knew that 100GB costs $X, 200GB costs $Y, 300GB costs $Z.

Linear regression just drew a line through all that. Now when a customer asks how much 350GB will cost, I just look at where 350 would be on that line and tell them. That’s literally how it works.

Logistic Regression – The One With A Confusing Name

So logistic regression sounds like regression but it’s actually for classification. I don’t name this stuff, I just deal with it. If linear regression is a straight line, logistic regression is an S-shaped curve. And this curve gives you probabilities instead of yes or no.

Think about spam filters. The filter doesn’t just go “spam” or “not spam.” It goes “there’s an 85% chance this is spam.” That percentage matters because the email system can then decide – maybe anything over 95% is definitely spam, but between 70-95% ask the user to confirm.

We use this at HostGet for security stuff. Maybe a system flags something as “probably suspicious network activity” at 78% confidence versus “definitely a hack attempt” at 99% confidence. That confidence level changes how we respond.

K-Nearest Neighbors – Super Simple But Actually Works

K-Nearest Neighbors, we call it KNN. This one I love explaining because once you get it, you get it immediately. The whole idea is: you want to make a prediction? Look at the things closest to it. Make your prediction based on those.

That’s the entire algorithm. Let’s say you want to guess how tall someone is. You have a database of people’s heights. A new person walks in. KNN goes “who is this person most similar to?” Finds five similar people. Looks at their heights. Averages them. That’s the prediction.

For classification it’s the same. You’ve got data labeled as “cat person” or “dog person.” New person comes in. KNN finds five similar people, takes a vote. Four out of five are cat people? The algorithm guesses cat person.

The trick is deciding how many similar things to look at. Too few and you’re basically just memorizing your training data. Too many and you’re averaging so much stuff together that the prediction gets meaningless.

I’ve spent way too much time tweaking this number on projects. You just gotta try different numbers until you find what works.

Support Vector Machine – Drawing The Right Line

SVM sounds fancy but here’s the real idea: you have two groups of data. You need to draw a line separating them. SVM doesn’t just draw any line – it draws the line with the biggest space between the two groups on either side.

Why does the space matter? Because that line is more likely to correctly identify new things you’ve never seen before. You’re not cutting it too close.

I use this for security stuff at HostGet. We need to separate normal network patterns from hack attempts. SVM finds that perfect line that keeps them maximally separated.

There’s this cool thing about SVM called kernel functions. Basically it lets you handle complicated relationships without you explicitly telling it what to look for. It figures out complex patterns automatically. That’s genuinely smart.

Naive Bayes – The Surprisingly Good Simple Approach

Naive Bayes is called “naive” because it makes an assumption that’s probably not true – it assumes all your features are independent. Like it pretends that being tall has nothing to do with whether you like cats or dogs. Obviously real life is more complicated than that.

But here’s the weird thing – even though the assumption is wrong, the algorithm works really well. Especially for text stuff like spam filters.

You feed it spam emails and non-spam emails. It learns what words show up in spam. Then when a new email comes in, it looks at all the words and calculates the probability it’s spam. I’ve seen it work better than way more complicated algorithms. Sometimes simple is just better. The algorithm doesn’t overthink it.

Decision Trees – The One You Can Actually Understand

Decision trees are my favorite when I need to explain WHY the algorithm made a decision. It’s basically a series of yes or no questions. “Is the temperature above 80? If yes go here if no go there. Is the humidity above 60?” You keep going until you get an answer.

The computer builds these questions automatically by looking at your data and trying to create groups that make sense. You want groups where almost everything in that group is the same type of thing.

I used this recently to help the team understand what patterns we were using to classify different customer issues. The tree was readable, we could see the logic, we could even manually check if it made sense. Try doing that with a neural network – you can’t.

When One Algorithm Isn’t Enough

Random Forests – A Committee Making Decisions

Random Forests are when you take a bunch of decision trees, train each one on slightly different data, and let them all vote on the final answer. Instead of asking one person for advice you ask a committee.

The randomness in training actually makes the overall model less likely to overfit because no single tree can dominate.

I use random forests all the time because they work for a lot of problems and they tell you which variables actually matter for the prediction. Want to know what’s driving your costs? Random forests will tell you.

Boosting – Models Learning From Mistakes

Boosting is different. You train models one at a time and each new model focuses on fixing the mistakes the previous model made. Like someone made an error and the next person is specifically there to correct that.

XGBoost is the famous one and honestly it’s become the standard for this kind of thing. The accuracy is genuinely impressive. Downside is it takes longer to train and it can overfit if you’re not careful. But when it works, it really works.

Neural Networks – The Game Changer

Alright so neural networks are the thing everyone talks about now and yeah, they’re genuinely powerful. Here’s what’s actually happening: neural networks learn complex patterns by building up from simpler patterns. Kind of like how humans learn.

Let me give you a real example. Say you’re trying to recognize handwritten numbers. A regular algorithm just sees pixels – this one is dark, this one is light. The problem is different people write “5” differently. The exact same pixels aren’t lit up every time.

A neural network does something different. It creates hidden layers that learn to recognize more complex ideas. First layer learns edges – “there’s a diagonal line here.” Next layer learns shapes – “this is a curved corner.” Deeper layers recognize full concepts – “this is the number 5.”

We don’t tell it to learn about curves or numbers. It figures it out. When you stack a lot of these layers you get deep learning. Each layer learns more complex ideas from the previous layer.

But here’s my honest take: neural networks aren’t magic. They need tons of data. They need serious computing power.

And you can see they work but you can’t easily explain why they made a specific decision. Don’t use them just because they sound cool. Use them when simpler stuff actually doesn’t work.

Unsupervised Learning – Finding Hidden Patterns

K-Means – Grouping Stuff Without Instructions

K-Means is when you want to organize your data into groups but you don’t know what those groups should be.

You tell it “I want 5 groups.” It picks 5 random starting points as group centers. It assigns everything to the closest center. Recalculates where the centers should be based on what’s actually in each group. Assigns everything again. Keeps going until nothing changes.

The hard part is deciding how many groups you actually want. You basically just gotta try numbers and see what makes sense for your situation.

Principal Component Analysis – Getting Rid Of Useless Features

PCA is for when you have too many features and want to throw away the ones that don’t matter. Like imagine analyzing fish – you look at length, height, and color. But length and height basically measure the same thing. They’re highly correlated. Do you really need both? Probably not.

PCA finds the directions in your data where stuff varies the most and focuses on those. It can simplify your data massively while keeping important information. Then when you use that simpler data in other algorithms, those algorithms work faster and sometimes better.

How I Actually Pick An Algorithm

Okay so real talk. Here’s how I actually choose:

First question: What am I predicting? A number or a category? That tells me regression or classification.

Second: Do I have labeled training data? Yes? Supervised. No? Unsupervised.

Third: How much data do I have? Not much? Keep it simple. Mountains of data? Maybe neural networks.

Fourth: Does anyone need to understand why? If yes, pick something you can explain. If it’s just “get the best accuracy,” you have more options.

Fifth: What computing power do I have? Neural networks need serious computers. Linear regression runs on basically anything.

Start with simple stuff. Seriously. Linear regression fixes a crazy amount of real problems. I’ve seen it beat fancy neural networks because the relationship in the data was just actually linear. If simple doesn’t work, try random forests or SVM. If those don’t cut it, then think about neural networks.

What I Learned The Hard Way

When I was starting out I felt pressure to use the most sophisticated algorithm. Like if I used linear regression I wasn’t a “real” data scientist. That’s backwards thinking.

The best people I’ve worked with use the simplest algorithm that solves the problem. They test multiple approaches and pick what actually works for their specific situation. They can explain their choice.

At HostGet we optimize cloud costs, find security issues, predict usage patterns – all kinds of stuff. Sometimes it’s linear regression. Sometimes random forests. Sometimes neural networks. But I’ve never gone “I MUST use a neural network” just because it sounds cool.

Machine learning algorithms are just tools. You wouldn’t use a power drill to hang a picture. You’d use a hammer. Same thing here – pick the right tool for the job.

You now have enough knowledge to make that decision. You understand supervised versus unsupervised. You know regression from classification. You can explain why you’d pick different algorithms for different problems.

Start simple. Test it on real data. See what happens. Try something more complex if you need to. That’s how you actually learn this stuff.