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Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning
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完成時間大約為55 小時

建議:7 hours/week...
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字幕:英語(English), 中文(簡體), 希伯來語, 西班牙語(Spanish), 印地語, 日語...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
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....
Reading
5 個視頻(共 42 分鐘), 9 個閱讀材料, 1 個測驗
Video5 個視頻
Welcome6分鐘
What is Machine Learning?7分鐘
Supervised Learning12分鐘
Unsupervised Learning14分鐘
Reading9 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
Quiz1 個練習
Introduction10分鐘
完成時間(小時)
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....
Reading
7 個視頻(共 70 分鐘), 8 個閱讀材料, 1 個測驗
Video7 個視頻
Cost Function8分鐘
Cost Function - Intuition I11分鐘
Cost Function - Intuition II8分鐘
Gradient Descent11分鐘
Gradient Descent Intuition11分鐘
Gradient Descent For Linear Regression10分鐘
Reading8 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
Quiz1 個練習
Linear Regression with One Variable10分鐘
完成時間(小時)
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....
Reading
6 個視頻(共 61 分鐘), 7 個閱讀材料, 1 個測驗
Video6 個視頻
Addition and Scalar Multiplication6分鐘
Matrix Vector Multiplication13分鐘
Matrix Matrix Multiplication11分鐘
Matrix Multiplication Properties9分鐘
Inverse and Transpose11分鐘
Reading7 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
Quiz1 個練習
Linear Algebra10分鐘
2
完成時間(小時)
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....
Reading
8 個視頻(共 65 分鐘), 16 個閱讀材料, 1 個測驗
Video8 個視頻
Gradient Descent for Multiple Variables5分鐘
Gradient Descent in Practice I - Feature Scaling8分鐘
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
Reading16 個閱讀材料
Setting Up Your Programming Assignment Environment8分鐘
Accessing MATLAB Online and Uploading 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
Quiz1 個練習
Linear Regression with Multiple Variables10分鐘
完成時間(小時)
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....
Reading
6 個視頻(共 80 分鐘), 1 個閱讀材料, 2 個測驗
Video6 個視頻
Moving Data Around16分鐘
Computing on Data13分鐘
Plotting Data9分鐘
Control Statements: for, while, if statement12分鐘
Vectorization13分鐘
Reading1 個閱讀材料
Lecture Slides10分鐘
Quiz1 個練習
Octave/Matlab Tutorial10分鐘
3
完成時間(小時)
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. ...
Reading
7 個視頻(共 71 分鐘), 8 個閱讀材料, 1 個測驗
Video7 個視頻
Hypothesis Representation7分鐘
Decision Boundary14分鐘
Cost Function10分鐘
Simplified Cost Function and Gradient Descent10分鐘
Advanced Optimization14分鐘
Multiclass Classification: One-vs-all6分鐘
Reading8 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
Quiz1 個練習
Logistic Regression10分鐘
完成時間(小時)
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. ...
Reading
4 個視頻(共 39 分鐘), 5 個閱讀材料, 2 個測驗
Video4 個視頻
Cost Function10分鐘
Regularized Linear Regression10分鐘
Regularized Logistic Regression8分鐘
Reading5 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
Quiz1 個練習
Regularization10分鐘
4
完成時間(小時)
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. ...
Reading
7 個視頻(共 63 分鐘), 6 個閱讀材料, 2 個測驗
Video7 個視頻
Neurons and the Brain7分鐘
Model Representation I12分鐘
Model Representation II11分鐘
Examples and Intuitions I7分鐘
Examples and Intuitions II10分鐘
Multiclass Classification3分鐘
Reading6 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
Quiz1 個練習
Neural Networks: Representation10分鐘
4.9
22,101 個審閱Chevron Right
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創建者 JPOct 25th 2016

Great course. A progressive discovery of the maths inner to the learning algorithms. This course gives that insight many ML practitioners don't have and is so important for making real use cases work.

創建者 SBSep 27th 2018

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

講師

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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 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....

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