What Is Machine learning (ML) – Machine learning (ML) is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

Why focus on Machine Learning (ML) now – Machine learning is all around us. We all use machine learning systems every day – such as spam filters, recommendation engines, language translation services, chat bots and digital assistants, search engines, and fraud detection systems.  It will soon be normal for machine learning systems to drive our cars, and help doctors to diagnose and treat our illnesses.  It’s important that kids are 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.  Machine Learning for Kids is a useful tool for introducing children to how ML systems are trained, how they are used, and some of the real-world implications of AI applications. For additional teaching and learning resources, including teaching the internals of ML systems, please head over to – <Machine Learning for Kids>.

Prerequisites – Listed below are the prerequisites for this course.

1. To work on this development track you will need to create an account at <Machine Learning for Kids> .
2. You can create an account for free at <Machine Learning for Kids> . There’s absolutely no cost involved. You might consider dropping Dale (Author- <Machine Learning for Kids>) an email and thank him for the wonderful work he’s doing.
3. For parents/teachers/educators/Code Club leads –
   1. If you are an parent/educator/teacher/code club lead/etc., select the sign-up option at the <Machine Learning for Kids> page.
   2. The select the relevant option i.e. “A parent, teacher or leader of a code club”.
   3. We would then recommend selecting the “Managed Class Account” option.
   4. By selecting this option you will be able to get Dale Lane (Author – <Machine Learning for Kids>) to help you out with creation of each of the accounts required for you/your class to work on this development track.

About this course – The Machine Learning for Kids course is being built by Dale Lane using APIs from IBM Watson. Dale has done an amazing job putting together the resources for this course, creating integration with the back-end Machine Learning engine (IBM Watson) and designing a system (https://machinelearningforkids.co.uk/) through which he’s able to provision access to anyone who wants to take this course. This course is entirely web-based and requires no installs or complicated setup to be able to use.  It was designed for use in the classroom by schools and volunteer-run coding groups for children, and provides an admin page for teachers or group leaders to be able to manage and administer access for their students.  For more details about the implementation, you can see the source code on GitHub, or read some of the blog posts about the tech.

The Machine Learning for Kids course introduces machine learning by providing hands-on experiences for training machine learning systems and building things with them.  It provides an easy-to-use guided environment for training machine learning models to recognize text, numbers, images, or sounds.  This builds on existing efforts to introduce and teach coding to children, by adding these models to educational coding platforms Scratch and App Inventor, and helping children create projects and build games with the machine learning models they train.