In case you haven’t heard…
The Gradient Descent now has a YouTube Channel. A fun way to start 2026, no?
Today’s post is extra special because it’s in conjunction with my very first YouTube video on The Gradient Descent’s new channel.
Watch it here:
Today’s post is the video in a text-formatted nutshell. So whether you prefer reading the cliff notes or hearing me explain with visuals and examples (or maybe both), you’ll get the same easy-to-follow breakdown in both formats.
📗 What Is Machine Learning, Anyway?
By now, you’ve probably heard the term “AI” so many times it’s practically the Taylor Swift of the tech world. Everywhere and impossible not to hear about.
But under AI’s shiny veneer lies something called Machine Learning. Simply put, it’s the process of teaching computers to learn from experience.
Instead of giving them detailed, step-by-step instructions, we feed them examples, let the algorithms work in the background, and over time, they figure out patterns on their own.
👨👩👧👦 The AI Family Tree
Yes, there’s a family tree in AI. These aren’t all the “members,” since the family is growing as we speak. But they’re certainly the prominent ones:
AI: This is the broad “parent”, the Godfather of building systems that act smart.
Machine Learning: This is like the favorite “child” of AI, the technology that enables computers to actually learn.
Deep Learning: This is like ML’s “overachieving” cousin. These brain-inspired neural networks are made for spotting insanely complex patterns.
👩🍳 The Four Main Ingredients Of ML
Machine Learning is a lot like following a recipe:
Data
Your raw ingredients. Without data, Machine Learning is just a chef with an empty fridge. And not just any data either, it needs to be accurate, relevant, and clean. We want to work with premium choice, not your local mystery meat.
Algorithms
The cooking method. Algorithms are the step-by-step instructions to turn raw data into something meaningful.
Model
The finished dish. Once the algorithm processes the data, you get *drum roll: the model! This is the “brain” that can now make predictions, classifications, or recommendations based on what it’s learned.
Training & Evaluation
The taste test. Training teaches the model to recognize patterns; evaluation checks if it learned anything useful. Together, they form the practice and performance review phase of ML.
📚 The Four Types Of Machine Learning
Not all ML learns the same way:
Supervised Learning: These are like teacher-led sessions. Here, labeled examples (“this is an apple”) are used to help the model make accurate predictions.
Unsupervised Learning: This is like detective work. There are no labeled examples here. Instead, the model tries to find similar patterns in the data.
Reinforcement Learning: This is when the model goes full gamer mode. Trial and error with rewards and penalties is the name of the game here. The model learns through experience until performance improves.
Semi-Supervised Learning: This is the hybrid. Some labeled data and a lot of unlabeled data allow the model to figure out the rest.
📚 Conclusion
To sum up ML in an extreme nutshell:
Quality data + smart algorithms + repeated practice = a system that learns and improves.
It’s the reason why your spam filter works, why Netflix keeps you up at night, and why self-driving cars keep getting better.
So go ahead — drop the knowledge you learned here today in your next convo. Instant credibility.
P.S., Since this post pairs with my first YouTube video, you can watch the same concepts explained with visuals. If you enjoy it, show some love with a like and a subscribe!
