From self-driving cars to determining someone's age, artificial intelligence (AI) systems trained with machine learning (ML) are being used more and more. But what is AI, and what does machine learning actually involve?
Who is it for?
On this four-week course you'll learn about different types of machine learning, and use online tools to train your own AI models.You'll delve into the problems that machine learning can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a machine learning model.
Explore the different types of machine learning.
The first week of this course will guide you through how you can use machine learning to label data, whether to work out if a comment is positive or negative or to identify the contents of an image.
Then you'll look at machine learning algorithms that create models to give a numerical output, such as predicting house prices based on information about the house and its surroundings.
You'll also explore other types of machine learning that are designed to discover connections and groupings in data that humans would likely miss, giving you a deeper understanding of how machine learning can be used.
Use tools to develop and train your own AI
During this course, you'll also investigate the different ways that the machine learning actually takes place.
You'll compare supervised learning, which uses training data labelled with the desired outcome, to unsupervised learning, where the aim of the machine learning is to spot new connections.
In the final week of the course, you'll investigate neural networks; a type of machine learning inspired by the structure of the brain that is used by many state-of-the-art AI systems such as YOTI's age determination algorithm.
Classification and making predictions Data Science and Machine Learning Supervised and unsupervised learning Neural networks Ethics and Machine Learning
Demonstrate several working machine learning models
Explain the different types of machine learning, and the problems that they are suitable for
Compare supervised, unsupervised, and reinforcement learning
Discuss the ethical issues surrounding machine learning and AI