Lesson 1 – What Is Machine Learning

What Is Machine learning (ML) – Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Check out the following videos to dive into the basics of Machine Learning.

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>


  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 learning resources for this course, developing integration with the back-end Machine Learning computing engine (IBM Watson) and designing a web based system (<Machine Learning for Kids>) through which he’s able to provision access to anyone who wants to take this course. Thank you Dale!!!! Much appreciated.

This course is entirely web-based and requires no installs or complicated setup to be able to use. Machine Learning for Kids 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.