Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

# 기계 학습

제공자:

## 기계 학습

## About this Course

### 학습자 경력 결과

## 40%

## 38%

### 귀하가 습득할 기술

### 학습자 경력 결과

## 40%

## 38%

#### 100% 온라인

#### 유동적 마감일

#### 완료하는 데 약 56시간 필요

#### 영어

## 강의 계획 - 이 강좌에서 배울 내용

**완료하는 데 2시간 필요**

## Introduction

Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.

**완료하는 데 2시간 필요**

**5개의 동영상**

**9개의 읽기 자료**

**1개 연습문제**

**완료하는 데 2시간 필요**

## Linear Regression with One Variable

Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.

**완료하는 데 2시간 필요**

**7개의 동영상**

**8개의 읽기 자료**

**1개 연습문제**

**완료하는 데 2시간 필요**

## Linear Algebra Review

This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.

**완료하는 데 2시간 필요**

**6개의 동영상**

**7개의 읽기 자료**

**1개 연습문제**

**완료하는 데 3시간 필요**

## Linear Regression with Multiple Variables

**완료하는 데 3시간 필요**

**8개의 동영상**

**16개의 읽기 자료**

**1개 연습문제**

**완료하는 데 5시간 필요**

## Octave/Matlab Tutorial

This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.

**완료하는 데 5시간 필요**

**6개의 동영상**

**1개의 읽기 자료**

**1개 연습문제**

**완료하는 데 2시간 필요**

## Logistic Regression

Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.

**완료하는 데 2시간 필요**

**7개의 동영상**

**8개의 읽기 자료**

**1개 연습문제**

**완료하는 데 4시간 필요**

## Regularization

Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.

**완료하는 데 4시간 필요**

**4개의 동영상**

**5개의 읽기 자료**

**1개 연습문제**

**완료하는 데 5시간 필요**

## Neural Networks: Representation

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

**완료하는 데 5시간 필요**

**7개의 동영상**

**6개의 읽기 자료**

**1개 연습문제**

### 검토

#### 4.9

##### 기계 학습의 최상위 리뷰

It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.

Everything is taught from basics, which makes this course very accessible- still requires effort, however will leave you with real confidence and understanding of subjects covered. Great teacher too..

Very nicely explained the mathematical topics, even for people like me with some phobia regarding large formulas. Useful hands-on experience with MATLAB coding, which I would have had to learn anyway.

this course is very basic. does not explain the concepts in details. Course instructor is very nice.\n\nLooking forward for a course in depth of machine learning and related algorithms from Andrew ng.

Andrew Ng is a great teacher.\n\nHe inspired me to begin this new chapter in my life. I couldn't have done it without you\n\nand also He made me a better and more thoughtful person.\n\nThank You! Sir.

Great explanation of each topic. However i felt the course is little outdated and it would have been better if it has topics related to python/R algorithm class libraries and algorithm implementation.

This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

Very helpful and easy to learn. The quiz and programming assignments are well designed and very useful. Thank Prof. Andrew Ng and coursera and the ones who share their problems and ideas in the forum.

Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts

Enjoyed following the course (videos) and reading notes, resources, discussions as well as doing assignments using GNU Octave (visualizing the results). Well organized. A big thanks to the whole team.

### 스탠퍼드 대학교 정보

## 자주 묻는 질문

강의 및 과제를 언제 이용할 수 있게 되나요?

강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

이 수료증을 구매하면 무엇을 이용할 수 있나요?

수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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