A Layman’s Explainer Of Machine Learning

A Child-Friendly Guide

Imagine teaching a computer to learn independently, just like a really smart pupil! That’s basically what machine learning is all about. It is a field of technology in which computers improve their abilities by analyzing large amounts of data.

The basic idea is that you feed a computer a large amount of data, such as photographs of cats and dogs. The computer then utilizes its lightning-fast intellect to determine the differences between them. The more data it sees, the better it becomes at distinguishing between cats and dogs – all without requiring someone to write a million lines of code. 

It enables computers to do a variety of fantastic things, such as recommend movies you might enjoy and recognize your face when you unlock your phone. It’s like having an extremely helpful friend who gets smarter the more you show them. 

Who Founded the Concept of Machine Learning?

Even though machine learning (ML) is popular nowadays, the principle has been around for a long time. Think back to 1949. A scientist named Donald Hebb authored a book explaining how our brains function. That book actually influenced how we develop machine learning systems today. 

A few years later, in 1952, Arthur Samuel, a computer programmer, entered the scene. He was very interested in artificial intelligence (AI), and he developed a checkers software that improved as it played more games. This program was similar to a super student in that it learned from its mistakes and experiences.

To select the optimal steps, the algorithm employed a technique known as ‘alpha-beta pruning’. Imagine the program acting as a super referee, checking the checkerboard. It would first determine who was winning and then decide which moves made the most sense to win the game. This approach evolved into the Minimax algorithm, which is an extremely smart way to determine the optimum move.

What Is Machine Learning in Simple Terms?

Imagine you’re showing a friend a collection of animal photos. You point at a fluffy puppy and say “That’s a dog!” Then you show them a picture of stripes and remark, “That’s a zebra!” The more photographs you show them, the better they’ll be able to distinguish between a fuzzy pal and a stripy grazer, correct?

That’s similar to machine learning. It is a method of teaching computers to learn from information, similar to viewing a large number of photos. The more data they encounter, the better they become at identifying patterns and solving problems on their own. We don’t have to write a million lines of code; instead, we give them examples, and they improve over time.

machine learning
Source: Cloud Mantra

What Is a Relationship Between AI and Machine Learning?

Imagine a giant toolbox filled with all kinds of clever ways to make machines behave a bit more human-like. This toolbox is called Artificial Intelligence (AI). Inside it you’ll find all kinds of tools, from voice assistants that chat with you on your phone to robot vacuums that clean your floors. Machine Learning (ML) is a specific set of tools in the AI ​​toolbox.

Here is how ML works, teaching a friend a new game by example. You can show him different winning moves and explain the rules. Machine learning works in a similar way, but for computers: We give them loads of information like images and numbers, and they use their super-fast brains to find patterns and make sense of things on their own.

What Are the Types of Machine Learning?

Machine Learning, like AI, contains sub-concepts that allow it to function efficiently; let us elaborate on these.

Supervised Learning

In machine learning, supervised learning is analogous to a study session with a computer buddy. You feed it a lot of information, such as photographs with labels (cats are labeled “cat” and canines are labeled “dog”). 

The computer then employs its super-brain to learn the patterns and identify the differences between these images.The more information it receives, the better it becomes at distinguishing cats from dogs on its own. 

It’s like your computer pal has progressed from needing labels to becoming an expert at recognizing furry friends! Supervised learning allows computers to predict things based on what they have already learnt. Pretty awesome, right?

Unsupervised Learning

In machine learning, supervised learning is analogous to showing a child a picture of a house and saying, “Build this!” Unsupervised learning is different. You simply provide the computer with a large amount of information, such as images or numbers, and allow it to explore on its own. 

The machine searches for patterns and relationships in that data on its own. It’s as if the computer is playing detective, attempting to find out what makes things similar and distinct. This can be handy for arranging photographs by color or grouping news articles that cover related topics.


Semi-supervised learning saves the day! It trains a machine learning model using both labeled and unlabeled data. So you pick a few flashcards with animal labels and blend them with a large quantity of unsorted photographs. The computer can still learn from labeled data, but it can also use unlabeled data to discover patterns and connections on its own. 

It’s like your friend looking at the unsorted photographs and attempting to determine which are cats based on what they’ve learnt from the flashcards. Semi-supervised learning allows you to develop models with less labeled data, which saves time and effort. 

Future Is Machine Learning and Beyond

Machine learning (ML) is a fascinating topic in which computers learn from data. But how do they learn? In this kid-friendly introduction, we’ll look at several machine learning approaches, breaking down difficult concepts into simple and familiar terms. 

We’ll see how supervised learning is like having a study session with a computer pal, unsupervised learning is like setting a toddler wild with a box of Legos, and semi-supervised learning is a brilliant combination that saves time and effort. 

So, plunge in and experience the thrilling world of machine learning, where computers become super students, data detectives, and pattern-finding experts.

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