{"id":938630,"date":"2021-10-18T10:00:35","date_gmt":"2021-10-18T14:00:35","guid":{"rendered":"https:\/\/www.prime-wow.com\/?p=938630"},"modified":"2021-10-18T10:00:35","modified_gmt":"2021-10-18T14:00:35","slug":"learn-the-fundamentals-of-ai-and-machine-learning-with-our-free-online-course","status":"publish","type":"post","link":"https:\/\/www.prime-wow.com\/?p=938630","title":{"rendered":"Learn the fundamentals of AI and machine learning with our free online course"},"content":{"rendered":"

Join our free online course Introduction to Machine Learning and AI<\/a> to discover the fundamentals of machine learning and learn to train your own machine learning models using free online tools.<\/p>\n

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Although artificial intelligence (AI) was once the province of science fiction, these days you\u2019re very likely to hear the term in relation to new technologies, whether that\u2019s facial recognition, medical diagnostic tools, or self-driving cars, which use AI systems to make decisions or predictions.<\/p>\n

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By the end of this free online course, you will have an appreciation for what goes into machine learning and artificial intelligence systems \u2014 and why you should think carefully about what comes out.<\/p>\n<\/blockquote>\n

Machine learning \u2014 a brief overview<\/h2>\n

You\u2019ll also often hear about AI systems that use machine learning (ML). Very simply, we can say that programs created using ML are \u2018trained\u2019 on large collections of data to \u2018learn\u2019 to produce more accurate outputs over time. One rather funny application you might have heard of is the \u2018muffin or chihuahua?\u2019 image recognition task<\/a>.<\/p>\n

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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 \u2018finished\u2019 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.<\/p>\n

It\u2019s important to understand AI and ML \u2014 especially for educators<\/h2>\n

But how does all this actually work? If you don\u2019t know, it\u2019s hard to judge what the impacts of these technologies might be, and how we can be sure they benefit everyone \u2014 an important discussion<\/a> that needs to involve people from across all of society. Not knowing can also be a barrier to using AI, whether that\u2019s for a hobby, as part of your job, or to help your community solve a problem.<\/p>\n

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For teachers and educators it\u2019s 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<\/a> and how they impact their lives<\/a>. (We\u2019ve also got a free seminar series<\/a> about teaching these topics.)<\/p>\n

To help you understand the fundamentals of AI and ML, we\u2019ve put together a free online course: Introduction to Machine Learning and AI<\/a>. Over four weeks in two hours per week, you\u2019ll learn how machine learning can be used to solve problems, without going too deeply into the mathematical details. You\u2019ll also get to grips with the different ways that machines \u2018learn\u2019, and you will try out online tools such as Machine Learning for Kids<\/a> and Teachable Machine<\/a> to design and train your own machine learning programs.<\/p>\n

What types of problems and tasks are AI systems used for?<\/h2>\n

As well as finding out how these AI systems work, you\u2019ll look at the different types of tasks that they can help us address. One of these is classification \u2014 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.<\/p>\n

You\u2019ll also learn about other types of tasks ML programs are used for, such as regression (predicting a numerical value from a continuous range) and knowledge organisation (spotting links between different pieces of data or clusters of similar data). Towards the end of the course you\u2019ll 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).<\/p>\n

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Before an ML program can be trained, you need to collect data to train it with. During the course you\u2019ll see how tools from statistics and data science are important for ML \u2014 but also how ethical issues can arise both when data is collected and when the outputs of an ML program are used.<\/p>\n

By the end of the course, you will have an appreciation for what goes into machine learning and artificial intelligence systems \u2014 and why you should think carefully about what comes out.<\/p>\n

Sign up to the course today, for free<\/h2>\n

The Introduction to Machine Learning and AI course is open for you to sign up to now. Sign-ups will pause after 12 December. Once you sign up, you\u2019ll have access for six weeks. During this time you\u2019ll be able to interact with your fellow learners, and before 25 October, you\u2019ll also benefit from the support of our expert facilitators. So what are you waiting for?<\/p>\n

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I want to sign up to the course<\/a><\/div>\n<\/div>\n

Share your views as part of our research<\/h3>\n

As part of our research on computing education<\/a>, we would like to find out about educators\u2019 views on machine learning. Before you start the course, we will ask you to complete a short survey. As a thank you for helping us with our research, you will be offered the chance to take part in a prize draw for a \u00a350 book token<\/strong>!<\/p>\n

Learn more about AI, its impacts, and teaching learners about them<\/h2>\n

To develop your computing knowledge and skills, you might also want to:<\/p>\n