Informações sobre o curso
4.9
90,650 classificações
23,130 avaliações
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 55 horas para completar

Sugerido: 7 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês, Chinês (simplificado), Hebraico, Espanhol, Hindi, Japonês...

Habilidades que você terá

Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 55 horas para completar

Sugerido: 7 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês, Chinês (simplificado), Hebraico, Espanhol, Hindi, Japonês...

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 42 mín.) min), 9 leituras, 1 teste
Video5 videos
Welcome6min
What is Machine Learning?7min
Supervised Learning12min
Unsupervised Learning14min
Reading9 leituras
Machine Learning Honor Code8min
What is Machine Learning?5min
How to Use Discussion Forums4min
Supervised Learning4min
Unsupervised Learning3min
Who are Mentors?3min
Get to Know Your Classmates8min
Frequently Asked Questions11min
Lecture Slides20min
Quiz1 exercício prático
Introduction10min
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 70 mín.) min), 8 leituras, 1 teste
Video7 videos
Cost Function8min
Cost Function - Intuition I11min
Cost Function - Intuition II8min
Gradient Descent11min
Gradient Descent Intuition11min
Gradient Descent For Linear Regression10min
Reading8 leituras
Model Representation3min
Cost Function3min
Cost Function - Intuition I4min
Cost Function - Intuition II3min
Gradient Descent3min
Gradient Descent Intuition3min
Gradient Descent For Linear Regression6min
Lecture Slides20min
Quiz1 exercício prático
Linear Regression with One Variable10min
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 61 mín.) min), 7 leituras, 1 teste
Video6 videos
Addition and Scalar Multiplication6min
Matrix Vector Multiplication13min
Matrix Matrix Multiplication11min
Matrix Multiplication Properties9min
Inverse and Transpose11min
Reading7 leituras
Matrices and Vectors2min
Addition and Scalar Multiplication3min
Matrix Vector Multiplication2min
Matrix Matrix Multiplication2min
Matrix Multiplication Properties2min
Inverse and Transpose3min
Lecture Slides10min
Quiz1 exercício prático
Linear Algebra10min
Semana
2
Horas para completar
3 horas para concluir

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 vídeos (total de (Total 65 mín.) min), 16 leituras, 1 teste
Video8 videos
Gradient Descent for Multiple Variables5min
Gradient Descent in Practice I - Feature Scaling8min
Gradient Descent in Practice II - Learning Rate8min
Features and Polynomial Regression7min
Normal Equation16min
Normal Equation Noninvertibility5min
Working on and Submitting Programming Assignments3min
Reading16 leituras
Setting Up Your Programming Assignment Environment8min
Access MATLAB Online and Upload the Exercise Files3min
Installing Octave on Windows3min
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10min
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3min
Installing Octave on GNU/Linux7min
More Octave/MATLAB resources10min
Multiple Features3min
Gradient Descent For Multiple Variables2min
Gradient Descent in Practice I - Feature Scaling3min
Gradient Descent in Practice II - Learning Rate4min
Features and Polynomial Regression3min
Normal Equation3min
Normal Equation Noninvertibility2min
Programming tips from Mentors10min
Lecture Slides20min
Quiz1 exercício prático
Linear Regression with Multiple Variables10min
Horas para completar
5 horas para concluir

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 vídeos (total de (Total 80 mín.) min), 1 leitura, 2 testes
Video6 videos
Moving Data Around16min
Computing on Data13min
Plotting Data9min
Control Statements: for, while, if statement12min
Vectorization13min
Reading1 leituras
Lecture Slides10min
Quiz1 exercício prático
Octave/Matlab Tutorial10min
Semana
3
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 71 mín.) min), 8 leituras, 1 teste
Video7 videos
Hypothesis Representation7min
Decision Boundary14min
Cost Function10min
Simplified Cost Function and Gradient Descent10min
Advanced Optimization14min
Multiclass Classification: One-vs-all6min
Reading8 leituras
Classification2min
Hypothesis Representation3min
Decision Boundary3min
Cost Function3min
Simplified Cost Function and Gradient Descent3min
Advanced Optimization3min
Multiclass Classification: One-vs-all3min
Lecture Slides10min
Quiz1 exercício prático
Logistic Regression10min
Horas para completar
4 horas para concluir

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 vídeos (total de (Total 39 mín.) min), 5 leituras, 2 testes
Video4 videos
Cost Function10min
Regularized Linear Regression10min
Regularized Logistic Regression8min
Reading5 leituras
The Problem of Overfitting3min
Cost Function3min
Regularized Linear Regression3min
Regularized Logistic Regression3min
Lecture Slides10min
Quiz1 exercício prático
Regularization10min
Semana
4
Horas para completar
5 horas para concluir

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 vídeos (total de (Total 63 mín.) min), 6 leituras, 2 testes
Video7 videos
Neurons and the Brain7min
Model Representation I12min
Model Representation II11min
Examples and Intuitions I7min
Examples and Intuitions II10min
Multiclass Classification3min
Reading6 leituras
Model Representation I6min
Model Representation II6min
Examples and Intuitions I2min
Examples and Intuitions II3min
Multiclass Classification3min
Lecture Slides10min
Quiz1 exercício prático
Neural Networks: Representation10min
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Benefício de carreira

37%

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Melhores avaliações

por AQMar 3rd 2018

An amazing skills of teaching and very well structured course for people start to learn to the machine learning. The assignments are very good for understanding the practical side of machine learning.

por OKApr 18th 2018

You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

Instrutores

Avatar

Andrew Ng

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

Sobre Universidade de Stanford

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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