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Machine Learning – Explained for a 10-Year Old

Children today are increasingly exposed to concepts of AI and machine learning at a very young age. Sometimes, it is not as easy for them to understand as we think and can turn to you for help.

This is a challenge if you personally have never explored machine learning.

But we have got you covered with our quick introduction to what is machine learning without getting into greasy details. It will not only help you develop a firm grasp of machine learning but will excite your child to explore these concepts with you.

What is Machine Learning? What’s the correlation between AI and ML?

Machine learning is a subset of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning programs use algorithms to analyze data and extract meaning from it.

“AI involves machines performing tasks that are attributes of human intelligence.

Machine learning is essentially a simple method of achieving artificial intelligence.”

Well, to simplify!

Machine learning allows computers to perform tasks without you having to tell the computer how to do it. It is all about computers learning to perform a task from a lot of examples and trying to figure out how to make the best judgement.

The way machines learn how to make the best decisions is by self-correcting its wrong guesses. This is done by looking at the correct answers in examples.

With machine learning, to get the computer to perform a complex task, you run the whole game by providing the right set of examples, the way to detect mistakes, etc.

For example, say you want to teach a child how to get a ball through a hoop.

In a traditional programming approach, you would give a series of detailed instructions. Such as, what angle to put hands at. How much to bend the knee. How fast to move.

You would tell exactly what you want them to do, how you want them to do it, how fast you want them to do it and the order that they should do things in.

In a machine learning sort of approach, you would show lots of examples of getting a ball through a hoop.

Maybe examples of different people attempting to get the basketball through the hoop.

Instead of telling them what to do, you would get them to learn from the collected examples of people who are already able to score.

The next question here is –  “if you don’t guide the computer, what do you do in this and how does the machine learn?”

Machine Learning is not performed by computers on their own. Basically, the only thing a machine is doing is making guesses based on examples and correcting itself to the best of its ability. Everything else is guided by the information and code you provide.

“So how does it learn?”

Just like a human, a computer can learn from three sources. 

  • Observing what others did in similar situations.
  • Observing a situation and trying to come up with the best possible logic on the spot to decide/conclude.
  • Learning from previous mistakes/successes. 

These three methods correspond to three branches of Machine learning, Supervised, Unsupervised and Reinforcement learning respectively. 

Supervised Learning:

In Supervised Learning, a computer can see the characteristics and value of 5 houses and come up with the value of the 6th house if its characteristics are known.

Or, it can tell what word in a sentence is the name of a country or city, given there are example sentences that may or may not contain names of cities or countries. Plus, every occurrence of a country and city name is tagged in these examples.

Unsupervised Learning:

Unsupervised is where you ask the computer to make decisions based on raw data attributes and a set of measurable quantities. Some examples would include asking a computer to come up with localities in a dataset where the latitude and longitude of a house are given. It would use Lat-Long to find distances and form localities of houses. 

You can also ask it to come up with a shortened version of a blog post, based on the number of words in the post. Note that no decisions made by others are given to the computer. As you can imagine, these methods might not be exactly close to human subjectivity. Unlike the supervised learning model, unsupervised learning models would make decisions based on a few mathematical quantities you ask them to.

Reinforcement Learning:

This is the third type of learning. Under this method, the computer starts with making random decisions and then learns based on errors it makes and successes it encounters as it goes. A recent discovery was an algorithm that could play many different arcade games after learning the correct/wrong moves. Reinforcement learning algorithms start by making a lot of mistakes in the beginning and then get better as they go.

Why Machine Learning for Kids?

Machine learning has been around for a while and it’s only growing in popularity. It can do some pretty awesome stuff, like identifying faces, mapping our brain activity, and even learning how to play chess!

There are many reasons why machine learning must be learned by children, but it all boils down to one thing: improving their life and future. According to accountancy firm PwC, AI will lead to the generation of 7.2 million jobs, which is a net gain of 2,00,000 jobs.

Machine learning is all around us and has many applications in the world today. 

From helping medical researchers identify new drugs for disease treatments to enabling internet companies such as Amazon and Netflix to understand viewer preferences. It’s also used for things like detecting fraud in financial transactions, spam filters, language translation services, search engines, chatbots and digital assistants.

It is also used by programmers in their daily work because it can automate repetitive tasks and improve accuracy.

Conclusion:

We need to educate our children about the future. Machine learning is one of the most important skills they need to learn. These machines will be taking over jobs, so we have to teach children how to work with them so that they gain a competitive advantage. Introducing and explaining machine learning concepts to children will help prepare them for the future and their future careers.

Machine learning doesn’t have to intimidate kids! 

Kids must be aware of how our world works. The best way to understand the capabilities and implications is to be able to build with this technology for themselves.

If the technologies of today fascinate your child – Strengthen their foundation with LearnPanda Smart Tech Course designed by leading curriculum creators and led by industry experts for kids aged 12-14.

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