Machine Learning enables machines to learn on their own, without being instructed by someone on every single step. Rather than writing a program step by step, you give the computer lots of examples (referred to as data), and it figures out the rules for itself.
Two substantial processes: training and testing.
Training Stage
Suppose you visit the market and select some mangoes. For every mango, you note its color, size, shape, where it was cultivated, and by whom it was sold. You also note how sweet, how juicy, or how ripe it is.
You feed this to the computer. It looks at the mangoes and determines how certain characteristics (such as color or size) correspond to their quality (such as sweetness or ripeness).
Testing Phase
Later, when you revisit the market, you notice fresh mangoes. You collect the same type of information about them, but still, you don't know if they are good or bad. This is your test data.
You provide this new information to the computer. Based on whatever it already knows, it will then guess whether or not those mangoes are sweet, juicy, or ripe. Now you can purchase quality mangoes without guessing!
Getting Smarter Over Time
The highlight? The computer gets better when it is given more data. If it gets something wrong, it can change its own rules. This is known as reinforcement learning.
You may use the same to test for apples, bananas, or grapes. Just train the computer on fruit data, and it will help you pick the best for you every time.
AI, ML, and Deep Learning – What's the Difference?
The majority of individuals confuse Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning. They think that the three are the same — but here is the catch, they are not. Let us break it down in the simplest terms:
Artificial Intelligence (AI)
AI is a big concept. It is making computers act like human beings — like thinking, learning, or reasoning. When a computer is doing something intelligent, like recognizing speech or a game, that is AI.
Machine Learning (ML)
ML is a branch of AI. It's where we teach computers using data. Instead of teaching the computer step by step what to do, we teach it a lot of information, and it learns from the data to be able to make decisions on its own.
Deep Learning
Deep Learning is still a smaller part of ML. It makes use of particular software called neural networks, which work in some ways much like our brains. These networks can enable computers to understand complex things such as images, words, and language better than regular ML.
Fast Summary:
• AI = The big idea: smart machines
• Machine learning is a subfield of AI that is data-driven.
• Deep Learning = A unique type of ML involving brain-like models
Learning Supervised and Unsupervised Learning
What is Supervised Learning?
Supervised Learning is when a computer is taught by examples that both contain the question and the answer. It is like learning from a teacher who provides you with the problems but also shows you the correct answers.
How Supervised Learning Works
In supervised learning, we give the machine lots of training examples. Each example is a pair of input data (e.g., numbers, images, or sound) and the correct result.
What is Unsupervised Learning?
Unsupervised Learning
In unsupervised learning, we provide the computer with lots of data but don't tell the computer what the right answer is. The computer learns to identify patterns, similarities, or groups in the data.
Unsupervised Learning – Real-Life Examples
Let's see some basic examples to get an idea of unsupervised learning.
1. Encountering New People at a Party
Consider that your friend invites you to a party and you do not know anyone there. You do not know anything about them, so you continue to try and sort people out in your mind.
You can try it by observing:
• Their clothes
• Their age
• How they communicate
• Their behaviour
Even if you don't know their name or origin, you categorize them by what you can see. This is what a computer does in unsupervised learning — it detects patterns on its own, without being shown the "right" group.
2. Viewing a Football Game for the First Time
Suppose you have never seen football before, but you watch a game on the Internet. You start to recognize patterns:
Some players wear the same shirt — you see them as one team :
• One person wears different clothes and uses his hands to touch the ball — you know he’s the goalkeeper.
• Another person dressed in black seems to be responsible for the game — maybe the referee.
• Some players keep moving ahead — they might be attackers. Others stay behind — they could be defenders.
• You've just applied unsupervised learning to cluster players based on what you observed — without being instructed by rules.
What is Reinforcement Learning?
Think of it as experimentation and trial and error — the same by which we learn new things by trying, failing, and getting better.
Here's how it works:
• The machine takes action.
• If the action is right, it gets a reward point.
• If the action is wrong, it is assigned a penalty point.
• The machine learns through experience what is good and what is bad and tries to do better and better.
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Conclusion
Machine learning allows computers to learn from information and improve themselves automatically over time without being explicitly instructed. It involves techniques like supervised, unsupervised, and reinforcement learning. Machine learning drives most of the smart devices that simplify our day-to-day lives.
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