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Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

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1
完成時間為 2 小時

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.

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5 個視頻 (總計 42 分鐘), 9 個閱讀材料, 1 個測驗
5 個視頻
Welcome6分鐘
Supervised Learning12分鐘
Unsupervised Learning14分鐘
9 個閱讀材料
Machine Learning Honor Code8分鐘
What is Machine Learning?5分鐘
How to Use Discussion Forums4分鐘
Supervised Learning4分鐘
Unsupervised Learning3分鐘
Who are Mentors?3分鐘
Get to Know Your Classmates8分鐘
Frequently Asked Questions11分鐘
Lecture Slides20分鐘
1 個練習
Introduction10分鐘
完成時間為 2 小時

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.

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7 個視頻 (總計 70 分鐘), 8 個閱讀材料, 1 個測驗
7 個視頻
Cost Function - Intuition II8分鐘
Gradient Descent11分鐘
Gradient Descent Intuition11分鐘
Gradient Descent For Linear Regression10分鐘
8 個閱讀材料
Model Representation3分鐘
Cost Function3分鐘
Cost Function - Intuition I4分鐘
Cost Function - Intuition II3分鐘
Gradient Descent3分鐘
Gradient Descent Intuition3分鐘
Gradient Descent For Linear Regression6分鐘
Lecture Slides20分鐘
1 個練習
Linear Regression with One Variable10分鐘
完成時間為 2 小時

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.

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6 個視頻 (總計 61 分鐘), 7 個閱讀材料, 1 個測驗
6 個視頻
Matrix Matrix Multiplication11分鐘
Matrix Multiplication Properties9分鐘
Inverse and Transpose11分鐘
7 個閱讀材料
Matrices and Vectors2分鐘
Addition and Scalar Multiplication3分鐘
Matrix Vector Multiplication2分鐘
Matrix Matrix Multiplication2分鐘
Matrix Multiplication Properties2分鐘
Inverse and Transpose3分鐘
Lecture Slides10分鐘
1 個練習
Linear Algebra10分鐘
2
完成時間為 3 小時

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.

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8 個視頻 (總計 65 分鐘), 16 個閱讀材料, 1 個測驗
8 個視頻
Gradient Descent in Practice II - Learning Rate8分鐘
Features and Polynomial Regression7分鐘
Normal Equation16分鐘
Normal Equation Noninvertibility5分鐘
Working on and Submitting Programming Assignments3分鐘
16 個閱讀材料
Setting Up Your Programming Assignment Environment8分鐘
Access MATLAB Online and Upload the Exercise Files3分鐘
Installing Octave on Windows3分鐘
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10分鐘
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3分鐘
Installing Octave on GNU/Linux7分鐘
More Octave/MATLAB resources10分鐘
Multiple Features3分鐘
Gradient Descent For Multiple Variables2分鐘
Gradient Descent in Practice I - Feature Scaling3分鐘
Gradient Descent in Practice II - Learning Rate4分鐘
Features and Polynomial Regression3分鐘
Normal Equation3分鐘
Normal Equation Noninvertibility2分鐘
Programming tips from Mentors10分鐘
Lecture Slides20分鐘
1 個練習
Linear Regression with Multiple Variables10分鐘
完成時間為 5 小時

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.

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6 個視頻 (總計 80 分鐘), 1 個閱讀材料, 2 個測驗
6 個視頻
Plotting Data9分鐘
Control Statements: for, while, if statement12分鐘
Vectorization13分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Octave/Matlab Tutorial10分鐘
3
完成時間為 2 小時

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.

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7 個視頻 (總計 71 分鐘), 8 個閱讀材料, 1 個測驗
7 個視頻
Cost Function10分鐘
Simplified Cost Function and Gradient Descent10分鐘
Advanced Optimization14分鐘
Multiclass Classification: One-vs-all6分鐘
8 個閱讀材料
Classification2分鐘
Hypothesis Representation3分鐘
Decision Boundary3分鐘
Cost Function3分鐘
Simplified Cost Function and Gradient Descent3分鐘
Advanced Optimization3分鐘
Multiclass Classification: One-vs-all3分鐘
Lecture Slides10分鐘
1 個練習
Logistic Regression10分鐘
完成時間為 4 小時

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.

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4 個視頻 (總計 39 分鐘), 5 個閱讀材料, 2 個測驗
4 個視頻
Regularized Logistic Regression8分鐘
5 個閱讀材料
The Problem of Overfitting3分鐘
Cost Function3分鐘
Regularized Linear Regression3分鐘
Regularized Logistic Regression3分鐘
Lecture Slides10分鐘
1 個練習
Regularization10分鐘
4
完成時間為 5 小時

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.

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7 個視頻 (總計 63 分鐘), 6 個閱讀材料, 2 個測驗
7 個視頻
Model Representation II11分鐘
Examples and Intuitions I7分鐘
Examples and Intuitions II10分鐘
Multiclass Classification3分鐘
6 個閱讀材料
Model Representation I6分鐘
Model Representation II6分鐘
Examples and Intuitions I2分鐘
Examples and Intuitions II3分鐘
Multiclass Classification3分鐘
Lecture Slides10分鐘
1 個練習
Neural Networks: Representation10分鐘
4.9
26502 個審閱Chevron Right

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來自机器学习的熱門評論

創建者 WZApr 3rd 2018

Very nice course,. Give a fundamental knowledge of machine learning in a clear, logic and easy-to-understand way. Suitable for those who has relatively weak background of math and statistics to learn.

創建者 MLAug 19th 2017

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.

講師

Avatar

Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain

關於 斯坦福大学

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....

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