I still remember the first time I tried to build a “smart” stock predictor back in 2015. I threw a massive Neural Network at a simple spreadsheet of historical prices. The result? It predicted the market would crash every single Tuesday.
Why? Because I used a sledgehammer to crack a nut. I was using the wrong algorithm for the data I had.
After a decade of working as a data scientist and deploying models for fintech and retail clients, I’ve learned that AI isn’t magic—it’s just math finding patterns. But understanding which math to use is what separates a failed project from a successful product.
If you are trying to understand the backend of ChatGPT, or just want to know what “Random Forest” actually means, this guide covers what I’ve learned from the trenches.
⚡ Quick Summary: The “Too Long; Didn’t Read”
If you are skimming this on your phone, here are the 3 critical takeaways:
It’s Not All “Deep Learning”: 80% of business problems I solve are handled by simple algorithms like Linear Regression, not complex Neural Networks.
Data Dictates the Algorithm: If you don’t have labeled data (results), you can’t use Supervised Learning. You’re stuck with Unsupervised methods.
The “Black Box” Problem: The more powerful the algorithm (like Deep Learning), the harder it is to explain why it made a decision. This matters for compliance.
[IMAGE TIP: Take a photo of a messy whiteboard sketch showing a flowchart of data inputs leading to a decision output. This proves you actually map these architectures out.]
The Reality Check: What Is an Algorithm?
Forget the sci-fi movies. In my daily work, an algorithm is simply a set of instructions I give a computer to turn Input A into Output B.
Traditional Coding: I explicitly write: “If the customer buys coffee, suggest a muffin.”
AI Algorithm: I feed the computer 10,000 receipts. The computer figures out: “Hey, 85% of people who buy coffee also buy muffins. I’ll make that a rule.”
The AI writes the rule itself. That’s the magic.
The “Big Three” Categories of Learning
Before we look at specific algorithms, you need to know the style of learning. Whenever I scope a new project for a client, I ask myself: “How are we teaching this machine?”
1. Supervised Learning (The “Teacher” Method)
This is the most common type I use. We have the answer key.
How it works: I give the model data labeled “Cat” and “Dog.” It learns the difference.
My experience: I used this to build a spam filter. I fed the model 5,000 emails labeled “Spam” and 5,000 labeled “Safe.” It learned that words like “Lottery” and “Urgent” usually meant spam.
2. Unsupervised Learning (The “Pattern Hunter”)
We have no answer key. The AI has to figure it out alone.
How it works: I throw a million unlabelled customer records at the model and say, “Group these.”
My experience: I used this for a clothing retailer. The AI noticed that people who bought winter coats also tended to buy thick wool socks, grouping them into a “Cold Climate” segment without me ever telling it that category existed.
3. Reinforcement Learning (The “Gamer”)
This is trial and error.
How it works: The AI takes an action. If it’s good, I give it points (reward). If it’s bad, I subtract points (punishment).
My experience: This is rarely used in standard business apps, but it’s huge in robotics. I once dabbled with a bot to play a simple video game; it spent 4 hours running into a wall before it realized that moving left gave it points.
5 Common AI Algorithms (And When I Actually Use Them)
Here is the breakdown of the actual tools in my toolkit.
1. Linear Regression
Type: Supervised Complexity: Low This is the grandfather of AI. It draws a straight line through data points.
Real-World Use: I use this for sales forecasting. If you sold 100 units in Jan, 120 in Feb, and 140 in March, Linear Regression predicts 160 for April.
Why I love it: It is fast, cheap to run, and I can explain it to a CEO in 30 seconds.
2. Decision Trees / Random Forest
Type: Supervised Complexity: Medium Imagine playing “20 Questions.” Is it an animal? Yes. Does it swim? No.
Real-World Use: I built a loan approval system using this.
Question 1: Is credit score > 700? (Yes/No)
Question 2: Is income > $50k? (Yes/No)
The “Forest” twist: A “Random Forest” creates 100 decision trees and averages their answers. It is incredibly robust. In my tests, Random Forests rarely “overfit” (memorize) the data compared to other models.
3. K-Means Clustering
Type: Unsupervised Complexity: Low-Medium This algorithm groups data points into distinct “clusters” based on similarities.
Real-World Use: Cyber-security. I used K-Means to detect network anomalies. Normal traffic clustered in one area; a sudden spike in data transfer at 3 AM stood out as a separate, distant cluster. It flagged a potential hack instantly.
4. Neural Networks (Deep Learning)
Type: Supervised/Unsupervised Complexity: Very High This mimics the human brain using layers of “neurons.”
Real-World Use: Image recognition. When you upload a photo to Google Photos and it knows it’s a “Beach,” that’s a Convolutional Neural Network (CNN).
The Downside: These are resource hogs. When I train a deep neural net, my cloud computing bill usually spikes by 40%. You don’t need this for a simple Excel spreadsheet analysis.
5. Transformers (The GPT Tech)
Type: Self-Supervised Complexity: Extreme This is the tech behind ChatGPT, Gemini, and Claude. It relies on an “Attention Mechanism” to understand the context of words in a sentence.
My Experience: Fine-tuning these models is difficult. Unlike Linear Regression, where I can see the math, Transformers are “Black Boxes.” Sometimes they hallucinate, and fixing them requires massive amounts of quality data, not just code tweaks.
Who This Is NOT For
I want to be transparent about the limitations here.
If you have “Small Data”: If you only have 50 rows of data in Excel, do not use AI. Use standard statistics. AI algorithms generally need thousands of examples to find reliable patterns.
If you need 100% explainability: If you are in a highly regulated industry (like healthcare or law), be careful with Deep Learning. If the AI rejects a patient’s insurance claim, and you can’t explain exactly which variable caused the rejection, you could face legal trouble. Stick to Decision Trees.
My “Pre-Flight” Checklist for Choosing an Algorithm
Before I write a single line of Python, I go through this mental checklist. You should too.
Do I have labeled data?
Yes? -> Look at Supervised Learning (Regression, Random Forest).
No? -> Look at Unsupervised Learning (Clustering).
Is accuracy or speed more important?
Speed? -> Linear Regression or Decision Trees.
Accuracy (at all costs)? -> Neural Networks.
Is the data text/images or numbers?
Numbers? -> Random Forest is usually best.
Images/Text? -> You need Deep Learning.
AdSense & Safety Note
Caution: When downloading datasets to practice with (from sites like Kaggle or Hugging Face), always scan files for malware. Never execute
.pkl(pickle) files from untrusted sources, as they can execute arbitrary code on your machine.Cost Warning: If you decide to experiment with training Neural Networks on cloud platforms (AWS, Google Cloud, Azure), set budget alerts. I once accidentally left a training job running over the weekend and woke up to a $600 bill. Don’t make my mistake.
The Bottom Line
Understanding AI algorithms isn’t about memorizing complex calculus. It’s about understanding capabilities and limitations.
For 90% of the projects I see, the problem isn’t the algorithm—it’s the data. A simple algorithm with clean, high-quality data will beat a complex Neural Network with messy data every single time.
My advice? Start with a simple Decision Tree. It’s transparent, easy to visualize, and powerful enough for most business logic. Once you master that, you can move on to the heavy hitters.
Disclaimers: I am a data practitioner, not a financial advisor. Mention of specific technologies or methods reflects the landscape as of late 2025.
Discover more from Prowell Tech
Subscribe to get the latest posts sent to your email.
