课程信息
4.9
82,125 ratings
21,263 reviews
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. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....
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100% 在线课程

立即开始,按照自己的计划学习。
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建议:7 hours/week

完成时间大约为53 小时
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字幕:English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese

您将获得的技能

Machine LearningArtificial Neural NetworkMachine Learning AlgorithmsGnu Octave
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Clock

建议:7 hours/week

完成时间大约为53 小时
Comment Dots

English

字幕:English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese

教学大纲 - 您将从这门课程中学到什么

1

章节
Clock
完成时间为 2 小时

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....
Reading
5 个视频(共 42 分钟), 9 个阅读材料, 1 个测验
Video5 个视频
Welcome6分钟
What is Machine Learning?7分钟
Supervised Learning12分钟
Unsupervised Learning14分钟
Reading9 个阅读材料
Machine Learning Honor Code8分钟
What is Machine Learning?5分钟
How to Use Discussion Forums4分钟
Supervised Learning4分钟
Unsupervised Learning3分钟
Who are Mentors?3分钟
Get to Know Your Classmates8分钟
Frequently Asked Questions11分钟
Lecture Slides20分钟
Quiz1 个练习
Introduction10分钟
Clock
完成时间为 2 小时

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....
Reading
7 个视频(共 70 分钟), 8 个阅读材料, 1 个测验
Video7 个视频
Cost Function8分钟
Cost Function - Intuition I11分钟
Cost Function - Intuition II8分钟
Gradient Descent11分钟
Gradient Descent Intuition11分钟
Gradient Descent For Linear Regression10分钟
Reading8 个阅读材料
Model Representation3分钟
Cost Function3分钟
Cost Function - Intuition I4分钟
Cost Function - Intuition II3分钟
Gradient Descent3分钟
Gradient Descent Intuition3分钟
Gradient Descent For Linear Regression6分钟
Lecture Slides20分钟
Quiz1 个练习
Linear Regression with One Variable10分钟
Clock
完成时间为 2 小时

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....
Reading
6 个视频(共 61 分钟), 7 个阅读材料, 1 个测验
Video6 个视频
Addition and Scalar Multiplication6分钟
Matrix Vector Multiplication13分钟
Matrix Matrix Multiplication11分钟
Matrix Multiplication Properties9分钟
Inverse and Transpose11分钟
Reading7 个阅读材料
Matrices and Vectors2分钟
Addition and Scalar Multiplication3分钟
Matrix Vector Multiplication2分钟
Matrix Matrix Multiplication2分钟
Matrix Multiplication Properties2分钟
Inverse and Transpose3分钟
Lecture Slides10分钟
Quiz1 个练习
Linear Algebra10分钟

2

章节
Clock
完成时间为 3 小时

Linear Regression with Multiple Variables

What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression....
Reading
8 个视频(共 65 分钟), 16 个阅读材料, 1 个测验
Video8 个视频
Gradient Descent for Multiple Variables5分钟
Gradient Descent in Practice I - Feature Scaling8分钟
Gradient Descent in Practice II - Learning Rate8分钟
Features and Polynomial Regression7分钟
Normal Equation16分钟
Normal Equation Noninvertibility5分钟
Working on and Submitting Programming Assignments3分钟
Reading16 个阅读材料
Setting Up Your Programming Assignment Environment8分钟
Accessing MATLAB Online and Uploading the Exercise Files3分钟
Installing Octave on Windows3分钟
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10分钟
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3分钟
Installing Octave on GNU/Linux7分钟
More Octave/MATLAB resources10分钟
Multiple Features3分钟
Gradient Descent For Multiple Variables2分钟
Gradient Descent in Practice I - Feature Scaling3分钟
Gradient Descent in Practice II - Learning Rate4分钟
Features and Polynomial Regression3分钟
Normal Equation3分钟
Normal Equation Noninvertibility2分钟
Programming tips from Mentors10分钟
Lecture Slides20分钟
Quiz1 个练习
Linear Regression with Multiple Variables10分钟
Clock
完成时间为 5 小时

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....
Reading
6 个视频(共 80 分钟), 1 个阅读材料, 2 个测验
Video6 个视频
Moving Data Around16分钟
Computing on Data13分钟
Plotting Data9分钟
Control Statements: for, while, if statement12分钟
Vectorization13分钟
Reading1 个阅读材料
Lecture Slides10分钟
Quiz1 个练习
Octave/Matlab Tutorial10分钟

3

章节
Clock
完成时间为 2 小时

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. ...
Reading
7 个视频(共 71 分钟), 8 个阅读材料, 1 个测验
Video7 个视频
Hypothesis Representation7分钟
Decision Boundary14分钟
Cost Function10分钟
Simplified Cost Function and Gradient Descent10分钟
Advanced Optimization14分钟
Multiclass Classification: One-vs-all6分钟
Reading8 个阅读材料
Classification2分钟
Hypothesis Representation3分钟
Decision Boundary3分钟
Cost Function3分钟
Simplified Cost Function and Gradient Descent3分钟
Advanced Optimization3分钟
Multiclass Classification: One-vs-all3分钟
Lecture Slides10分钟
Quiz1 个练习
Logistic Regression10分钟
Clock
完成时间为 4 小时

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. ...
Reading
4 个视频(共 39 分钟), 5 个阅读材料, 2 个测验
Video4 个视频
Cost Function10分钟
Regularized Linear Regression10分钟
Regularized Logistic Regression8分钟
Reading5 个阅读材料
The Problem of Overfitting3分钟
Cost Function3分钟
Regularized Linear Regression3分钟
Regularized Logistic Regression3分钟
Lecture Slides10分钟
Quiz1 个练习
Regularization10分钟

4

章节
Clock
完成时间为 5 小时

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. ...
Reading
7 个视频(共 63 分钟), 6 个阅读材料, 2 个测验
Video7 个视频
Neurons and the Brain7分钟
Model Representation I12分钟
Model Representation II11分钟
Examples and Intuitions I7分钟
Examples and Intuitions II10分钟
Multiclass Classification3分钟
Reading6 个阅读材料
Model Representation I6分钟
Model Representation II6分钟
Examples and Intuitions I2分钟
Examples and Intuitions II3分钟
Multiclass Classification3分钟
Lecture Slides10分钟
Quiz1 个练习
Neural Networks: Representation10分钟
4.9
Direction Signs

39%

完成这些课程后已开始新的职业生涯
Briefcase

83%

通过此课程获得实实在在的工作福利

热门审阅

创建者 JSJun 17th 2017

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..

创建者 CCJun 20th 2018

good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)

讲师

Andrew Ng

Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

关于 Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States....

常见问题

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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