Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it. While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming, we believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should. The big AI breakthroughs sound like science fiction, but they come down to a simple idea: the use of data to train statistical algorithms. In this course you will learn to understand the basic idea of machine learning, even if you don't have any background in math or programming. Not only that, you will get hands on and use user friendly tools developed at Goldsmiths, University of London to actually do a machine learning project: training a computer to recognise images. This course is for a lot of different people. It could be a good first step into a technical career in Machine Learning, after all it is always better to start with the high level concepts before the technical details, but it is also great if your role is non-technical. You might be a manager or other non-technical role in a company that is considering using Machine Learning. You really need to understand this technology, and this course is a great place to get that understanding. Or you might just be following the news reports about AI and interested in finding out more about the hottest new technology of the moment. Whoever you are, we are looking forward to guiding you through you first machine learning project. NB this course is designed to introduce you to Machine Learning without needing any programming. That means that we don't cover the programming based machine learning tools like python and TensorFlow.
Machine Learning for All
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Ce que vous allez apprendre
You will understand the basic of how modern machine learning technologies work
You will be able to explain and predict how data affects the results of machine learning
You will be able to use a non-programming based platform train a machine learning module using a dataset
You will be able to form an informed opinion on the benefits and dangers of machine learning to society
Programme du cours : ce que vous apprendrez dans ce cours
Machine learning
In this week you will learn about artificial intelligence and machine learning techniques. You will learn about the problems that these techniques address and will have practical experience of training a learning model.
Data Features
This week you will learn about how data representation affects machine learning and how these representations, called features, can make learning easier.
Machine Learning in Practice
In this topic you will get ready to do your own machine learning project. You will learn how to test a machine learning project to make sure it works as you want it to. You will also think about some of the opportunities and dangers of machine learning technology.
Your Machine Learning Project
In this final topic you will do your own machine learning project: collecting a dataset, training a model and testing it.
Enseignant
Dr Marco Gillies
Senior LecturerÀ propos de Université de Londres
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
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