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.

# Mathematics for Machine Learning 專項課程

## Mathematics for Machine Learning。 Learn about the prerequisite mathematics for applications in data science and machine learning

## 本專項課程介紹

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.
The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require basic Python and numpy knowledge.
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.

製作方：

##### 3 courses

按照建議的順序或選擇您自己的順序。

##### 項目

旨在幫助您實踐和應用所學到的技能。

##### 證書

在您的簡歷和領英中展示您的新技能。

項目概覽

課程

- Beginner Specialization.
- No prior experience required.

### 第 1 門課程

## Mathematics for Machine Learning: Linear Algebra

- 課程學習時間
- 5 weeks of study, 2-5 hours/week

- 字幕
- 英語（English）

### 課程概述

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 h**您可以選擇只參加本課程。**了解更多。### 第 2 門課程

## Mathematics for Machine Learning: Multivariate Calculus

- 課程學習時間
- 6 weeks of study, 2-5 hours/week

- 字幕
- 英語（English）

### 課程概述

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 t**您可以選擇只參加本課程。**了解更多。### 第 3 門課程

## Mathematics for Machine Learning: PCA

- 課程學習時間
- 4 weeks of study, 4-5 hours/week

- 字幕
- 英語（English）

### 課程概述

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,**您可以選擇只參加本課程。**了解更多。

## 製作方

#### David Dye

##### Professor of Metallurgy

#### Samuel J. Cooper

##### Lecturer

#### Marc P. Deisenroth

##### Lecturer in Statistical Machine Learning

#### A. Freddie Page

##### Strategic Teaching Fellow

## FAQs

More questions? Visit the Learner Help Center.