Machine Learning
提供方
Machine Learning
Stanford University
課程信息
4.9
82,192 ratings
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.
您將獲得的技能
Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning
教學大綱 - 您將從這門課程中學到什麼
1
章節
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（共 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
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（共 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
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（共 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
章節
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（共 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
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
1 個練習
Linear Regression with Multiple Variables10分鐘
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（共 80 分鐘）, 1 個閱讀材料, 2 個測驗
6 個視頻
Basic Operations13分鐘
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
章節
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（共 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
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（共 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
章節
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（共 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
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創建者 JS•Jun 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..
創建者 CC•Jun 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)
講師
Andrew Ng
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/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.
常見問題
When will I have access to the lectures and assignments?
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.
What will I get if I purchase the Certificate?
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.
What is the refund policy?
Is financial aid available?
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