The Importance of Machine Learning for Kids - Camp K12 Magazine
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Why Machine Learning is Important for Kids

The world of machine learning isn’t limited to professionals in STEM fields, it’s one that our kids can very well explore! The importance of machine learning lies in the fact that it is a technology that is set to shape our future. That impact has already begun, with much of the technology we interact with on a day-to-day basis utilizing aspects of machine learning. Things like spam filters, translation services, chatbots, search engines, customized recommendations, and image & speech recognition involve machine learning and are a part of today’s regular life. It’s important for kids to understand how our world works, and the technology it runs on!

Content Table:

  • How Does Machine Learning Work?
  • Current applications of Machine Learning
  • How Machine Learning Will Impact Our Lives in the Future
  • 5 Reasons for Kids to Learn Machine Learning
  • Ready to get started on your journey into Machine Learning?
  • FAQs

How Does Machine Learning Work?

There are many, many applications that show the importance of machine learning in today’s world. Teaching kids about machine learning will not only help them become more future-ready, but it will also help them build their problem-solving skills and creativity. Before we dive into that, let’s take a moment to learn what machine learning actually is.

Machine learning is a subset of Artificial Intelligence, where we teach a ‘machine’ or algorithm to learn and improve by itself through data being explicitly programmed, and train it to give accurate results or predictions from the data it is provided with.

Breaking it down into steps:

1. We feed a large amount of data to a machine and teach it to look for patterns in that data. For example, the importance of machine learning is seen through a common application of it- distinguishing between spam mail and genuine mail (called ham).

2. The algorithm would be trained to look for certain keywords in all the mail that is received. Then, as the machine gets new data, it is able to recognise those patterns and apply them to predict outcomes.

3. After it has been trained, the algorithm is now able to recognise the keywords it’s been trained to look for, and can flag incoming mail as either spam or ham.

4. Over time, as the machine gets more and more data, it is able to improve its data model to predict outcomes more accurately. The algorithms most email services use these days are extremely accurate in flagging spam mails.

Machines have the ability to compute much larger volumes of data, much faster than human beings, which means that they can allow us to view information in new and different ways.

Current Applications of Machine Learning

Machine learning is now commonly used in many fields, and as more and more companies become aware of the benefits of machine learning its usage has increased. Machine learning algorithms have many applications- from social media to stock market trading. Here are some of the most common applications that show the importance of machine learning:

1. Image Recognition

Image recognition is one of the oldest applications that show the importance of machine learning . Here, the algorithm is fed a large number of images, each of which has its own label. The algorithm learns to associate certain images with certain labels. When faced with a new image, it is able to use this knowledge to classify the new image under a label. This is used in your photos app, or in most social media apps. 

When you upload a photo, the algorithm is able to pick up on the other people in the photo and suggests tagging them. It’s also able to pick up on the location and suggests a location tag. Your photos app can classify images into different albums such as ‘food’, ‘beaches’, and so on because it can identify the objects present in these images through a machine learning algorithm.

2. Product Recommendations

Most online shopping websites and applications make use of machine learning algorithms to suggest and recommend products to its users. The algorithm stores all the data on purchases a user has made in the past, as well as purchases other users have made, and looks for patterns within that data. It is then able to make recommendations to users based on these patterns. It is what’s behind the  ‘recommended for you’ sections and the ‘similar purchases’ sections we see often on online shopping websites.

As an example, imagine you make a purchase for a notebook. Through the data collected on past purchases and similar purchases by other users, the algorithm may recommend that you also buy pens. 

3. Video Recommendations

Similar to the above, websites like Youtube, Netflix, and Hostar track your viewing habits so they can make recommendations for you. Through a combination of what you have watched in the past and the viewing habits of other people in the same demographic as you, they are able to recommend videos or shows they think you are most likely to watch.

4. Automatic Language Translation

When you open a web page written in another language, you may notice a pop-up asking you if you would like to ‘translate it to English’. There is a machine learning algorithm behind this feature.

Google Translate uses the GNMT or Google Neural Machine Translation which is an algorithm that has been trained on thousands of languages and dictionaries. It uses something called Natural Language Processing to translate one language to another. This means it takes into account the order of the words and the sequences of words that appear together frequently and other such techniques to provide accurate translations. Since language translation is a complicated process, we are still working on improving automatic translators, but we have come a long way and we have some incredibly accurate language translation tools these days.

5. Stock Market Trading

Stock market trading is all about following and predicting trends. These days, many financial analysis companies are making use of machine learning algorithms to collect market data and make predictions about day-to-day and future market trends.

How Machine Learning Will Impact Our Lives in the Future

We all know of the importance of machine learning as it is used in many areas and fields currently, but there’s a lot of exciting work happening to expand its usage. 

1. Self-driving cars

Tesla is most famous for its work on creating self-driving cars. The aim is to use machine learning algorithms to process a video of the surroundings in real-time and detect obstacles like people and other objects in its path so it can avoid them.

2. Medical diagnosis

Medical diagnosis is a sensitive field that still relies largely on human judgment, but there have been vast strides to incorporate machine learning into this field. Machine learning uses a data-driven approach to making diagnoses. It would look at all the data collected about a single patient, and make predictions about any future health problems based on statistics, like say, identify if someone is prone to heart disease. Machine learning algorithms are also being used to distinguish between benign and malignant tumors.

5 Reasons for Kids to Learn Machine Learning

The importance of machine learning is becoming more clear- it’s everywhere! These technologies are shaping both our present and our future. What does this mean for our kids? Here are some of the benefits of learning machine learning:

1. Improve career prospects

As mentioned, machine learning is being used in many fields. It would be beneficial for kids to get started on learning about machine learning algorithms as it is likely that they will play a big role in improving their future career prospects.

2. Creativity and problem solving skills

Machine learning algorithms require creativity and thinking out of the box. For example, an algorithm is only as good as the dataset it is trained on, and one of the most challenging parts of building a machine learning system is to choose appropriate datasets that will give accurate results and reflect real world situations. Kids learn much more about other factors that make a good machine learning algorithm. 

3. Gain awareness about the world

The importance of machine learning is that it helps find solutions to real world problems. For example, the problem with most facial recognition software is that they are trained on narrow datasets that do not reflect the real world. Kids will learn about issues like the importance of racial diversity and develop social awareness.

4. Better coding skills

Naturally, one of the benefits of learning machine learning is that it gives kids a chance to improve their coding skills. Though there are many libraries available to use, it is always a good thing to attempt things the old-fashioned way and learn the logic behind the operation.

5. Better understanding of data analysis

Data is becoming an increasingly important part of all fields. Data analysis is one of the most important and in-demand skills currently. One of the benefits of machine learning is that it helps in getting comfortable with datasets, data collection and data analysis.

Ready to get started on your journey into Machine Learning?

You don’t need to wonder about how to  learn machine learning- there are a lot of great resources available on the internet. You can find a list of ways to get started here.
You can also check out Camp K12’s courses on machine learning, which are geared towards beginners.

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Most machine learning projects are written using Python. Python is a highly intuitive, beginner-friendly programming language. Additionally, there are many libraries available to handle some of the more complex operations. These are easy to use and well documented.

Math is an important part of machine learning. Machine learning algorithms are built upon a foundation of statistics. That said, you don’t need to be a math genius. A basic understanding of math is enough to learn machine learning. As mentioned above, there are many libraries available that will handle the complex math for you.

Artificial Intelligence refers to a branch of computer science that aims to create systems that can think and learn for themselves. Machine learning is a subset of artificial intelligence.