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 전문 분야

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

## 전문분야 소개

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in maths - 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 specialisation aims to bridge that gap, getting you up to speed in the underlying maths, 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 optimise 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 Components Analysis, uses the maths from the first two courses to do simple optimisation for the situation where you don’t have an understanding of how the data variables relate to each other.
At the end of this specialisation you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

생성자:

##### 3 courses

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##### 프로젝트

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##### 수료증

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프로젝트 개요

강좌

- Beginner Specialization.
- No prior experience required.

### 강좌 1

## Mathematics for Machine Learning: Linear Algebra

현재 세션: Aug 20- 약정
- 5 weeks of study, 2-5 hours/week

- 자막
- English

### 강좌 소개

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, an**이 강좌만 이용할 것을 선택할 수 있습니다.**자세히 알아보기.### 강좌 2

## Mathematics for Machine Learning: Multivariate Calculus

현재 세션: Aug 20- 약정
- 6 weeks of study, 2-5 hours/week

- 자막
- English

### 강좌 소개

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 thi**이 강좌만 이용할 것을 선택할 수 있습니다.**자세히 알아보기.### 강좌 3

## Mathematics for Machine Learning: PCA

현재 세션: Aug 20- 약정
- 4 weeks of study, 4-5 hours/week

- 자막
- English

### 강좌 소개

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 variance**이 강좌만 이용할 것을 선택할 수 있습니다.**자세히 알아보기.

## 제작자

#### 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.