Burhanettin Nacar

I remember the first time someone tried to explain machine learning to me. They started drawing hyperplanes on a whiteboard and talking about “gradient descent” within the first two minutes. I nodded along, pretending to understand, but honestly? I was completely lost.

Here’s what I wish someone had told me back then: these ML models are just pattern-matching techniques inspired by how we naturally solve problems. No magic, just math doing what our brains do every day. Let me show you what I mean.

Clustering - When Your Computer Organizes Your Mess

We all have that one folder on our desktop called “Stuff” or “To Sort”, right? Clustering is basically the algorithm that says “let me handle that mess for you.”

It looks at your 1,000 random songs and groups them without you labeling anything. The upbeat workout tracks end up together, chill piano music forms its own group, rock songs cluster separately. The algorithm just finds patterns in tempo, instruments, and style.

I love this because it’s unsupervised - you don’t have to teach it what “rock” means. It just figures it out. That’s the same tech behind how Netflix groups movies or how stores figure out which products are usually bought together.

Logistic Regression - The Yes/No Decision Maker

First off, terrible name. It sounds like something from a supply chain meeting. But it’s actually super simple: it’s the “Yes or No” machine.

Think about deciding whether to bring an umbrella. You check the clouds (80% coverage), humidity (super high at 95%), temperature, wind speed… Your brain weighs all this and concludes: “Yeah, bring the umbrella.” That’s exactly what logistic regression does, except it gives you a probability like 85%.

Your email spam filter? Logistic regression. Loan approvals? Same thing. It’s everywhere because most real-world problems boil down to binary choices.

Decision Trees - The Flowchart Algorithm

Remember those “Choose Your Own Adventure” books from when we were kids? Decision trees are exactly that, just with math.

You’re hungry. Very hungry? If yes, do you have 30 minutes? Yes means cook a meal, no means order takeout. Not very hungry? Want something healthy? And so on. Each answer branches to the next question until you land on a decision.

Hungry?
├─ Starving → Got time?
│   ├─ Yes → Cook
│   └─ No → Order pizza
└─ Meh → Healthy?
    ├─ Yes → Salad
    └─ No → Snacks

Those annoying customer service chatbots use these. So do credit scoring systems. Honestly, they’re kind of like how we naturally make decisions anyway.

Collaborative Filtering - Your Digital Friend’s Recommendation

This is basically digital peer pressure. You know the “people who bought this also bought that” section on Amazon? That’s this guy.

Here’s the idea: you and I both binge-watched The Office and love Thai food. I just discovered a new podcast and rated it 5 stars. The algorithm figures “hey, we have similar taste, so you’ll probably like this podcast too.”

It’s crowdsourcing recommendations. Spotify’s Discover Weekly? Same concept - finding people with your music taste and suggesting what they’re listening to. Sometimes it’s creepy accurate, sometimes it completely misses (no, Spotify, I don’t want to listen to polka just because I clicked one weird link). But when it works, it works well.

Neural Networks - The Brain-Inspired Stuff

Okay, this is where things get a little sci-fi. Neural networks are loosely inspired by how our brains work, but don’t let that scare you.

Feed-Forward Networks: One-Way Street

Think assembly line. Raw materials go in one end, each station does its thing, finished product comes out the other end. No going backwards.

Input → Hidden layers doing math → Output. That’s it.

These are the OG neural networks. They’re used for basic stuff like reading handwritten zip codes on envelopes or predicting house prices. Nothing fancy, but they work.

Recurrent Neural Networks (RNNs): The One With Memory

Feed-forward networks have the memory of a goldfish—they process one thing and immediately forget it. RNNs are different; they actually remember context.

When you read “Sarah went to the store. She bought eggs. Then she went home,” you know “she” refers to Sarah because you remember the first sentence. RNNs do the same thing - they carry context forward.

This is why your phone’s autocomplete works. It doesn’t just look at the current word, it remembers what you’ve been typing. Voice assistants use these too. The problem? RNNs are kind of forgetful with long sequences. They’re like that friend who remembers the beginning of a story but gets fuzzy on the middle parts.

Convolutional Neural Networks (CNNs): The Image Expert

If you’ve ever wondered how your phone knows which photo has your dog in it, this is the answer.

They look at images in layers, starting small and building up:

• First layer: detects edges and simple patterns
• Next layer: combines those into shapes
• Next: combines shapes into features like “eye” or “nose”
• Final layer: “Oh, this is a face!”

It’s honestly similar to how we look at pictures. You don’t immediately see “John” - your brain processes edges, then features, then puts it together. CNNs copy that approach.

Self-driving cars use these to spot pedestrians. Doctors use them to find tumors in X-rays. They’re really good at anything visual.

Transformers: The New Hotness

This is the big one. The reason everyone is freaking out about AI right now? It’s mostly because of these guys.

Transformers are what powers ChatGPT, Google Translate, and basically every modern AI that handles language.

The breakthrough? Attention mechanism. Instead of processing words one-by-one like RNNs, transformers look at everything simultaneously and figure out what’s important.

Imagine you’re at a party with multiple conversations happening. RNNs would eavesdrop on one conversation at a time. Transformers have super-hearing - they catch all conversations at once AND figure out that Person A in the kitchen is finishing Person B’s story from 20 minutes ago.

This “attention” to the whole context is why modern translation is so good. The model sees the entire sentence before deciding how to translate each word. Context matters, and transformers are phenomenal at context.

They’re also why we can have these scary-good AI writing tools now. The compute cost is high, but the results speak for themselves.

Why This Matters

It’s easy to get lost in the hype, but at the end of the day, these are just tools. Really smart tools, but tools nonetheless.

Your morning routine probably involves face unlock (CNN), checking if there’s traffic (RNNs predicting patterns), scrolling a personalized news feed (collaborative filtering). During lunch, your spam filter (logistic regression) is working. By evening, Netflix is recommending shows (collaborative filtering + neural networks), and you’re asking Siri something (transformers + RNNs).

The trick is knowing which tool fits which problem. You wouldn’t use a hammer to tighten a screw, right? Same deal here:

• Got images? CNN is your friend
• Dealing with sequences or text? Transformers or RNNs
• Need simple yes/no predictions? Logistic regression
• Want to find natural groupings? Clustering
• Building a recommendation system? Collaborative filtering

I still remember being completely overwhelmed by ML terminology. But once you strip away the jargon, these are just formalized versions of how we already think about problems. The math gets complicated, sure, but the core ideas? They’re intuitive.

If you want to dive deeper, pick one model and build something small with it. Trust me, there’s no better teacher than breaking things and fixing them yourself.