Machine Learning refers to artificial intelligence. It can also be identified as a subset of Artificial Intelligence (AI).
But what exactly is Machine Learning?
We know that humans have the ability to make decisions about
the future based on things we have learned and experiences we have gained.
Similarly, providing data to machines, allowing them to learn from that data,
and giving them the ability to make decisions is called Machine Learning.
For
example, if we take a dog and a cat, we have the ability to identify
which is which based on their differences.
 |
| Cat |
 |
| Dog |
We can write a traditional computer program to output
"dog" or "cat" based on a specific image provided
beforehand. However, that program won't be able to identify a new image it
hasn't seen before.
This is where we can create a Machine Learning model.
In this process, we "train" the machine by providing a large number
of images of dogs and cats categorized accordingly. Then, the machine can
recognize patterns—such as the shape, colors, and features of eyes—for both
dogs and cats. Once it recognizes these, it creates a pattern that
distinguishes one from the other.
If we train the model using a vast number of images this way,
the machine gains the ability to look at any new image of a dog or cat and
decide which one it is based on the characteristics it learned previously.
This is the core concept of Machine Learning.
Machine Learning Applications
1. Face recognition
2. Speech recognition
3. Email spam filtering
4. Online fraud detection
5. Product recommendations
Types of Machine Learning
We can divide the concept of Machine Learning into three main
types:
1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning
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