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.
제공자:
Machine Learning for All
런던 대학교이 강좌에 대하여
배울 내용
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
제공자:
런던 대학교
The University of London is a federal University which includes 18 world leading Colleges. Our distance learning programmes were founded in 1858 and have enriched the lives of thousands of students, delivering high quality University of London degrees wherever our students are across the globe. Our alumni include 7 Nobel Prize winners. Today, we are a global leader in distance and flexible study, offering degree programmes to over 50,000 students in over 180 countries. To find out more about studying for one of our degrees where you are, visit www.london.ac.uk
강의 계획 - 이 강좌에서 배울 내용
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.
검토
MACHINE LEARNING FOR ALL의 최상위 리뷰
THIS COURSE REALLY REALLY FOUND HELPFUL.AND THIS IS A PERFECT COURSE FOR A BEGINNER.REALLY GAINED AN INSIGHT INTO MACHINE LEARNING.BENEFITS AND DANGERS .AND THE INSTRUCTOR IS TOO TOO GOOD.LOTS OF LOVE
Very good course that simple and interactive explanation provides you with insight into machine learning world. After this you can confidently start more advanced courses on the topic. Thank You!
An wonderful course with basics on Machine Learning. Neatly done. Learned a lot and did some practical exercise too. Those who are interested in Machine Learning good to begin with this course.
An wonderful course with basics on Machine Learning. Neatly done. Learned a lot and did some practical exercise too. Those who are interested in Machine Learning good to begin with this course.
자주 묻는 질문
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more.
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