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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....
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
What is Machine Learning?7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....
7 個視頻 (總計 70 分鐘), 8 個閱讀材料, 1 個測驗
7 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
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....
6 個視頻 (總計 61 分鐘), 7 個閱讀材料, 1 個測驗
6 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
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....
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 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
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....
6 個視頻 (總計 80 分鐘), 1 個閱讀材料, 2 個測驗
6 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
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. ...
7 個視頻 (總計 71 分鐘), 8 個閱讀材料, 1 個測驗
7 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
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. ...
4 個視頻 (總計 39 分鐘), 5 個閱讀材料, 2 個測驗
4 個視頻
Cost Function10分鐘
Regularized Linear Regression10分鐘
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. ...
7 個視頻 (總計 63 分鐘), 6 個閱讀材料, 2 個測驗
7 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
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
5
完成時間為 5 小時

Neural Networks: Learning

In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition. ...
8 個視頻 (總計 78 分鐘), 8 個閱讀材料, 2 個測驗
8 個視頻
Backpropagation Algorithm11分鐘
Backpropagation Intuition12分鐘
Implementation Note: Unrolling Parameters7分鐘
Gradient Checking11分鐘
Random Initialization6分鐘
Putting It Together13分鐘
Autonomous Driving6分鐘
8 個閱讀材料
Cost Function4分鐘
Backpropagation Algorithm10分鐘
Backpropagation Intuition4分鐘
Implementation Note: Unrolling Parameters3分鐘
Gradient Checking3分鐘
Random Initialization3分鐘
Putting It Together4分鐘
Lecture Slides10分鐘
1 個練習
Neural Networks: Learning10分鐘
6
完成時間為 5 小時

Advice for Applying Machine Learning

Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models. ...
7 個視頻 (總計 63 分鐘), 7 個閱讀材料, 2 個測驗
7 個視頻
Evaluating a Hypothesis7分鐘
Model Selection and Train/Validation/Test Sets12分鐘
Diagnosing Bias vs. Variance7分鐘
Regularization and Bias/Variance11分鐘
Learning Curves11分鐘
Deciding What to Do Next Revisited6分鐘
7 個閱讀材料
Evaluating a Hypothesis4分鐘
Model Selection and Train/Validation/Test Sets3分鐘
Diagnosing Bias vs. Variance3分鐘
Regularization and Bias/Variance3分鐘
Learning Curves3分鐘
Deciding What to do Next Revisited3分鐘
Lecture Slides10分鐘
1 個練習
Advice for Applying Machine Learning10分鐘
完成時間為 1 小時

Machine Learning System Design

To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data. ...
5 個視頻 (總計 60 分鐘), 3 個閱讀材料, 1 個測驗
5 個視頻
Error Analysis13分鐘
Error Metrics for Skewed Classes11分鐘
Trading Off Precision and Recall14分鐘
Data For Machine Learning11分鐘
3 個閱讀材料
Prioritizing What to Work On3分鐘
Error Analysis3分鐘
Lecture Slides10分鐘
1 個練習
Machine Learning System Design10分鐘
7
完成時間為 5 小時

Support Vector Machines

Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice. ...
6 個視頻 (總計 98 分鐘), 1 個閱讀材料, 2 個測驗
6 個視頻
Large Margin Intuition10分鐘
Mathematics Behind Large Margin Classification19分鐘
Kernels I15分鐘
Kernels II15分鐘
Using An SVM21分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Support Vector Machines10分鐘
8
完成時間為 1 小時

Unsupervised Learning

We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points....
5 個視頻 (總計 39 分鐘), 1 個閱讀材料, 1 個測驗
5 個視頻
K-Means Algorithm12分鐘
Optimization Objective7分鐘
Random Initialization7分鐘
Choosing the Number of Clusters8分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Unsupervised Learning10分鐘
完成時間為 4 小時

Dimensionality Reduction

In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets. ...
7 個視頻 (總計 67 分鐘), 1 個閱讀材料, 2 個測驗
7 個視頻
Motivation II: Visualization5分鐘
Principal Component Analysis Problem Formulation9分鐘
Principal Component Analysis Algorithm15分鐘
Reconstruction from Compressed Representation3分鐘
Choosing the Number of Principal Components10分鐘
Advice for Applying PCA12分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Principal Component Analysis10分鐘
9
完成時間為 2 小時

Anomaly Detection

Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection. ...
8 個視頻 (總計 91 分鐘), 1 個閱讀材料, 1 個測驗
8 個視頻
Gaussian Distribution10分鐘
Algorithm12分鐘
Developing and Evaluating an Anomaly Detection System13分鐘
Anomaly Detection vs. Supervised Learning7分鐘
Choosing What Features to Use12分鐘
Multivariate Gaussian Distribution13分鐘
Anomaly Detection using the Multivariate Gaussian Distribution14分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Anomaly Detection10分鐘
完成時間為 4 小時

Recommender Systems

When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization....
6 個視頻 (總計 58 分鐘), 1 個閱讀材料, 2 個測驗
6 個視頻
Content Based Recommendations14分鐘
Collaborative Filtering10分鐘
Collaborative Filtering Algorithm8分鐘
Vectorization: Low Rank Matrix Factorization8分鐘
Implementational Detail: Mean Normalization8分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Recommender Systems10分鐘
10
完成時間為 1 小時

Large Scale Machine Learning

Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets....
6 個視頻 (總計 64 分鐘), 1 個閱讀材料, 1 個測驗
6 個視頻
Stochastic Gradient Descent13分鐘
Mini-Batch Gradient Descent6分鐘
Stochastic Gradient Descent Convergence11分鐘
Online Learning12分鐘
Map Reduce and Data Parallelism14分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Large Scale Machine Learning10分鐘
11
完成時間為 1 小時

Application Example: Photo OCR

Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system. ...
5 個視頻 (總計 57 分鐘), 1 個閱讀材料, 1 個測驗
5 個視頻
Sliding Windows14分鐘
Getting Lots of Data and Artificial Data16分鐘
Ceiling Analysis: What Part of the Pipeline to Work on Next13分鐘
Summary and Thank You4分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Application: Photo OCR10分鐘
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創建者 SSMay 17th 2019

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.

創建者 NNOct 15th 2016

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.

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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 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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