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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/uol-machine-learning-for-all
课程评论：没有评论
课程名称：全民机器学习 课程概述：机器学习，通常被称为人工智能（AI），是当前技术领域中最令人兴奋的领域之一。我们每天都会看到关于面部识别技术、自驾车或能够像真实人类对话的计算机的新闻报道。机器学习技术有望彻底改变人类生活和工作的几乎所有领域，因此会影响到我们的生活，大家都希望了解更多。机器学习以其复杂性而闻名，通常需要高级数学和工程技能来理解。虽然作为机器学习工程师确实涉及大量数学和编程，但我们相信任何人都可以理解机器学习的基本概念。考虑到这项技术的重要性，人人都应该有所了解。尽管大规模的人工智能突破听起来像科幻小说，但它们实际上源于一个简单的理念：利用数据训练统计算法。在本课程中，您将学习机器学习的基本思想，即使您没有数学或编程背景。此外，您将亲手使用由伦敦大学金匠学院开发的用户友好工具，实际进行一个机器学习项目：训练计算机识别图像。 本课程适合各类人群。它可以是探索机器学习技术职业生涯的第一步，毕竟在深入技术细节之前，先掌握高层次概念总是更好。此外，如果您的角色是非技术性的，例如管理者或其他考虑使用机器学习的公司内部人员，您也将需要理解这项技术，而本课程是了解这一技术的绝佳起点。即使您只是关注人工智能相关新闻并希望深入了解这一当下最热门的技术，本课程同样适合您。无论您是谁，我们期待着指导您完成第一个机器学习项目。 课程大纲： 1. 机器学习：您将学习人工智能和机器学习技术，以及这些技术所解决的问题，并获得训练学习模型的实践经验。 2. 数据特征：您将学习数据表示如何影响机器学习，并了解这些叫做特征的表示方式如何使学习变得更简单。 3. 机器学习实践：您将为自己的机器学习项目做好准备，了解如何测试机器学习项目以确保其按预期工作，并思考机器学习技术的一些机遇和风险。 4. 您的机器学习项目：在最后一部分，您将进行自己的机器学习项目：收集数据集、训练模型并进行测试。 本课程旨在在无需编程的情况下介绍机器学习，因此不会涉及基于编程的机器学习工具（如Python和TensorFlow）。
Name:Machine learning
Description: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.
Name:Data Features
Description:This week you will learn about how data representation affects machine learning and how these representations, called features, can make learning easier.
Name:Machine Learning in Practice
Description: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.
Name:Your Machine Learning Project
Description:In this final topic you will do your own machine learning project: collecting a dataset, training a model and testing it.
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.
