关于此 专项课程
100% 在线课程

100% 在线课程

立即开始,按照自己的计划学习。
灵活的计划

灵活的计划

设置并保持灵活的截止日期。
初级

初级

完成时间(小时)

完成时间大约为2 个月

建议 12 小时/周
可选语言

英语(English)

字幕:英语(English)...

您将获得的技能

Eigenvalues And EigenvectorsPrincipal Component Analysis (PCA)Multivariable CalculusLinear Algebra
100% 在线课程

100% 在线课程

立即开始,按照自己的计划学习。
灵活的计划

灵活的计划

设置并保持灵活的截止日期。
初级

初级

完成时间(小时)

完成时间大约为2 个月

建议 12 小时/周
可选语言

英语(English)

字幕:英语(English)...

专项课程 的运作方式

加入课程

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实践项目

每个专项课程都包括实践项目。您需要成功完成这个(些)项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程,则需要在开始之前完成其他所有课程。

获得证书

在结束每门课程并完成实践项目之后,您会获得一个证书,您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。

how it works

此专项课程包含 3 门课程

课程1

Mathematics for Machine Learning: Linear Algebra

4.6
1,735 个评分
304 个审阅
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning....
课程2

Mathematics for Machine Learning: Multivariate Calculus

4.7
883 个评分
136 个审阅
This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future....
课程3

Mathematics for Machine Learning: PCA

4.0
463 个评分
93 个审阅
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand machine learning algorithms....

讲师

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David Dye

Professor of Metallurgy
Department of Materials
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Samuel J. Cooper

Lecturer
Dyson School of Design Engineering
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A. Freddie Page

Strategic Teaching Fellow
Dyson School of Design Engineering
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Marc P. Deisenroth

Lecturer in Statistical Machine Learning
Department of Computing

关于 Imperial College London

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

常见问题

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  • 此课程完全在线学习,无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

  • 此专项课程不提供大学学分,但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。

  • 3/4 hours a week for 3 to 4 months

  • High school maths knowledge is required. Basic knowledge of Python can come in handy, but it is not necessary for courses 1 and 2. For course 3 (intermediate difficulty) you will need basic Python and numpy knowledge to get through the assignments.

  • We recommend taking the courses in the order in which they are displayed on the main page of the Specialization.

  • At the end of this Specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

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