# 数学在机器学习领域的应用 专项课程

数学在机器学习领域的应用. Learn about the prerequisite mathematics for applications in data science and machine learning

**45,843**人已注册！

### 您将获得的技能

## 关于此 专项课程

## 应用的学习项目

Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

#### 可分享的证书

#### 100% 在线课程

#### 灵活的计划

#### 初级

无需相关领域的预备知识无需相关经验。

#### 完成时间大约为2 个月

#### 英语（English）

### 专项课程的运作方式

### 加入课程

Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习，请直接注册专项课程，或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时，您将自动订阅整个专项课程。您可以只完成一门课程，您可以随时暂停学习或结束订阅。访问您的学生面板，跟踪您的课程注册情况和进度。

### 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。

### 此专项课程包含 3 门课程

### Mathematics for Machine Learning: Linear Algebra

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.

### Mathematics for Machine Learning: Multivariate Calculus

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.

### Mathematics for Machine Learning: PCA

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.

### 关于 伦敦帝国学院

### 审阅

#### 4.5

##### 来自数学在机器学习领域的应用的热门评论

Taught in an intuitive way. Never have I been able to understand linear algebra better than after following the first 3 weeks of this course. I can't wait to complete the entire specialization

Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.

Another great course from Imperial College London. I highly recommend this specialization.

I have thoroughly enjoyed every course of this specialization. Thank you very much.

It was a good course compared to other two courses of this specialization.

Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.

Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.

## 常见问题

退款政策是如何规定的？

我可以只注册一门课程吗？

可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。

有助学金吗？

我可以免费学习课程吗？

此课程是 100% 在线学习吗？是否需要现场参加课程？

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。

完成专项课程需要多长时间？

3/4 hours a week for 3 to 4 months

What background knowledge is necessary?

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.

Do I need to take the courses in a specific order?

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

完成专项课程后我会获得大学学分吗？

This is a non-credit Specialization.

What will I be able to do upon completing 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.

还有其他问题吗？请访问 学生帮助中心。