Types of Machine Learning — The first time I heard this sentence was quite recent. My professor wrote it on the board and I reasoned, great, another list of useless categories to memorise. However, the thing is that after I related these types to my everyday activities (such as Netflix recommendations, spam filters or even self-driving cars 🚗), everything became clear.
Did you ever find yourself wondering why does YouTube know what video I will watch next? or how does my bank prevent fraud in a few seconds? The answer has to do with the types of machine learning.
In this blog, I am going to take you through them as I would explain to a friend. Direct, straight and a touch personal. Let’s go!
Machine learning is of different types; what are those?
To get into the kinds of machine learning it is time to establish the scene. Machine learning (ML) simply refers to the process of teaching machines how to learn the patterns based on data. We do not write whether this or that, but we feed the machine data and it calculates things. Cool, right?
However, here is the twist to it all not every learning is the same. Machines learn like us human beings, who learn in various ways, through teachers, experiences or trial and error. The 5 types of machine learning enter the picture there.
1. Supervised Learning
It is the most widespread machine learning. In supervised learning we transform the model on labeled data (fancy word for already has answers).
MyLife example: Choose a situation when I was a child and was studying math. My teacher provided me with sums whose answers were in the back of the book. I figured out, verified and repaired myself. That is monitored learning at work.
Real-world uses:
- Gmail spam filter (spam vs. not spam).
- Predicting house prices 🏠.
- Health care diagnosis.
2. Unsupervised Learning – The Explorer 🔍️global.
Suppose there is no teacher. You end up in an unfamiliar city with no map and you simply walk around, spotting patterns. That is blind learning.
Mechanism: The machine examines un-labeled raw data and clusters items, according to their similarities.
My-day example: Spotify playlists 🎶. At other times I will say, Why does Spotify know I would love these songs together? Oh, that is clustering,–an unsupervised learning trick.
Real-world uses:
- Marketing customer segmentation.
- Basket analysis (why chips and Coke are so much together 🥤🍟).
- Fraud detection.
3. Semi-Supervised Learning
When I initially heard about semi-supervised learning I thought: This is me in exams half the syllabus already prepared, half vibes only. 😂
n this model, one receives a very small portion of labeled data and a huge portion of unlabeled data. It trains more cheaply and in less time than fully supervised models.
Sample: A physician who label a handful of X-rays as disease or no disease, and the machine compute the remainder.
Uses:
- Medical research 🧬.
- Voice recognition (a la Alexa or Siri).
- Web content classification.
4. Reinforcement Learning
This one is very personal since I am a gamer. In reinforcement learning machines learn through trial and error with rewards or penalties.
Life analogy: Can you recall the time in life when you touched a hot pan when you were young? 🔥 You learned that it was no longer to be touched. It is reinforcement learning.
Real-world uses:
- Self-driving cars 🚗.
- AI in games (AI win against human champions).
- Robotics.
One cool example? Google DeepMind trained a machine learning model to play Atari games only by rewarding points. There’s reinforced learning in action.
5. Self-Supervised Learning
This is currently one of the hottest machine learning. It is a kind of cousin of supervised learning, but does not require huge amounts of labeled data. The system, instead, forms labels out of the input information by default.
Case in point: Generation of missing words in a sentence – That is precisely how large language models (LLMs) such as ChatGPT are trained.
Uses:
- Natural language processing (NLP).
- Image recognition.
- Speech-to-text models.
🚀 That is the way AI is driving the revolution we are in today.
Quick Comparison of the Types of Machine Learning
| Type | Data | Example | Real-life use |
|---|---|---|---|
| Supervised | Labeled | Math teacher | Spam filters |
| Unsupervised | Unlabeled | Exploring a new city | Spotify playlists |
| Semi-supervised | Mix of both | Half-studied exam prep | Medical research |
| Reinforcement | Rewards/Penalties | Gaming | Self-driving cars |
| Self-supervised | Self-labeled | Fill-in-the-blank | ChatGPT, NLP |
Why Do You even care about the types of machine learning?
To be honest, I did not believe that when I first learned about machine learning types, I assumed that it was all theory. However, here is the reality- this stuff is what we live with every day:
- The ads you see on Instagram.
- The warning messages of your bank about fraud.
- How your iPhone clusters the photos of your best friend.
These forms of machine learning are not only understood by techies, but by anyone interested in the way that AI is influencing our world.
Final Thoughts
We discussed 5 categories of machine learning supervised, unsupervised, semi-supervised, reinforcement and self-supervised. They are each personalities of their own. And honestly? They’re all around us.
n case it is your first time with ML, supervised learning is the place to begin. In case you have questions about AI such as ChatGPT, consider self-supervised learning.
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The most important thing to realize about types of machine learning is that you would rather not read about it you would prefer to experiment with it. 🚀Here is the secret I wish someone had told me when I was a little younger: the best thing to do with types of machine learning is not to read about it but to get it running.
