Nowadays, we are surrounded by various AI and machine learning technologies, many of which we use directly or indirectly.
If you use Google or any other search engine to get here, that’s an example.
YouTube, Netflix, Amazon recommendations, self-learning cars, face lock on devices, autocorrect features, and many more.
However, everything started from a thought
Can machines think? —a question posed by Alan Turing, a British mathematician—sparked the beginning of artificial intelligence.
Later, Arthur Samuel, the father of machine learning, created the first machine learning program that taught itself by playing games.
Yes — that’s where it all began.
okay, what is actually Machine Learning?
What Is Machine Learning?
Just like the name says, machines try to learn from data and make decisions. It’s actually like imitating us.
In our childhood, we learned to recognize cats or dogs by seeing them many times.
When we see them again, we understand whether it’s a cat or a dog. The images we saw are stored in our minds, and we compare new ones with our memory. Similarly, machines also learn from data and try to identify or predict things, just like we do.
In the real world, a good example is unlocking a mobile with face scanning. When your phone captures your face for the first time, it stores the image and extracts features like nose shape or jawline, converting them into numbers.
Later, when you try to unlock it, the phone compares your live face with the stored data. If the match is above a certain threshold, it unlocks the phone — saying, “It’s you.”
So, what is data? Anything that carries enough information to help a machine learn and make decisions can be called data.
For example, this blog post could be part of the training data for a model that explains machine learning. But to train a good model, you’d need many such blog posts — from different sources.
Similarly, articles, tweets, photos, audio clips, videos, medical records, and transactions — all of these are forms of data. If they carry useful information, they can be used in machine learning.
Based on the type of data and the way we train the model, machine learning is generally classified into four categories.
- Supervised Machine Learning
- Unsupervised Learning
- Semi – Supervised
- Reinforcement
Supervised Machine Learning
A machine learns from labeled data to make predictions — this is called Supervised Machine Learning.
Labeled data means the data comes with a label or description.
For example, you might provide a dog image labeled “dog” or an audio clip labeled “music.”
This helps the machine understand what the data represents.
An example algorithm used in supervised learning is Linear Regression, which can be used to predict house prices.
Unsupervised Learning
This is the opposite of Supervised Learning. It uses only data — no labels or descriptions.
Here, we don’t tell the machine what to learn.
So, the machine needs to find patterns or groups within the data on its own.
For example, K-Means Clustering is an algorithm used for customer segmentation (grouping users based on behavior for targeted marketing).
Semi-Supervised Machine Learning
In this type, the machine is trained with a small amount of labeled data and a large amount of unlabeled data.
Semi-Supervised Machine Learning = Low volume of labeled data + Large volume of unlabeled data.
Example: Self-training + Logistic Regression
Reinforcement Learning
It’s like training a dog. If you tell it to sit and it does, you give it a cookie. Otherwise, no cookie. Just like that, a machine learns by trial and error to reach a specific goal — by getting rewards or not getting them.
eg: Self driving car uses reinforcement learning to improve their driving skills.
That’s all for today.
will be back.