Learn the fundamentals of AI and machine learning
Check out this free online course Introduction to AI and machine learning. To discover the fundamentals of machine learning and learn to train your own machine learning models using free online tools.
Although artificial intelligence (AI) was once the province of science fiction. These days you’re very likely to hear the term in relation to new technologies. Whether that’s facial recognition, medical diagnostic tools, or self-driving cars, which use AI systems to make decisions or predictions.
By the end of this free online course, you will have an appreciation for what goes into AI and machine learning systems. And why you should think carefully about what comes out.
Machine learning — a brief overview
You’ll also often hear about AI systems that use machine learning (ML). Very simply, we can say that programs created using ML are ‘trained’ on large collections of data to ‘learn’ to produce more accurate outputs over time.
One rather funny application you might have heard of is the ‘muffin or chihuahua?’ image recognition task.
More precisely, we would say that a ML algorithm builds a model. Based on large collections of data (the training data), without being explicitly programmed to do so. The model is ‘finished’ when it makes predictions or decisions with an acceptable level of accuracy. (For example, it rarely mistakes a muffin for a chihuahua in a photo.) It is then considered to be able to make predictions or decisions using new data in the real world.
It’s important to understand AI and machine learning — especially for educators
But how does all this actually work? If you don’t know, it’s hard to judge what the impacts of these technologies might be. And how we can be sure they benefit everyone. An important discussion that needs to involve people from across all of society. Not knowing can also be a barrier to using AI, whether that’s for a hobby, as part of your job, or to help your community solve a problem.
For teachers and educators it’s particularly important to have a good foundational knowledge of AI and ML, as they need to teach their learners what the young people need to know about these technologies and how they impact their lives. (We’ve also got a free seminar series about teaching these topics.)
This course was put together to help you understand the fundamentals of AI and ML. Over four weeks in two hours per week, you’ll learn how machine learning can be used to solve problems. Without going too deeply into the mathematical details. You’ll also get to grips with the different ways that machines ‘learn’. And you will try out online tools such as Machine Learning for Kids and Teachable Machine to design and train your own machine learning programs.
What types of problems and tasks are AI systems used for?
As well as finding out how these AI systems work, you’ll look at the different types of tasks that they can help us address. One of these is classification. Working out which group (or groups) something fits in. Such as distinguishing between positive and negative product reviews. Identifying an animal (or a muffin) in an image. Or spotting potential medical problems in patient data.
You’ll also learn about other types of tasks ML programs are used for. Such as regression which is the predicting a numerical value from a continuous range. And knowledge organization such as spotting links between different pieces of data or clusters of similar data. Towards the end of the course you’ll dive into one of the hottest topics in AI today: neural networks, which are ML models whose design is inspired by networks of brain cells (neurons).
Before an ML program can be trained, you need to collect data to train it with. During the course you’ll see how tools from statistics and data science are important for ML — but also how ethical issues can arise both when data is collected and when the outputs of an ML program are used.
By the end of the course, you will have an appreciation for what goes into machine learning and artificial intelligence systems — and why you should think carefully about what comes out.
https://www.raspberrypi.org/blog/fundamentals-ai-machine-learning-free-online-course/
