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Inglés (English)

Subtítulos: Chino (simplificado), Inglés (English), Hebreo, Español (Spanish), Hindi, Japonés...

Habilidades que obtendrás

Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

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Inglés (English)

Subtítulos: Chino (simplificado), Inglés (English), Hebreo, Español (Spanish), Hindi, Japonés...

Programa - Qué aprenderás en este curso

Semana
1
2 horas para completar

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 videos (Total 42 minutos), 9 readings, 1 quiz
5 videos
Welcome6m
What is Machine Learning?7m
Supervised Learning12m
Unsupervised Learning14m
9 lecturas
Machine Learning Honor Code8m
What is Machine Learning?5m
How to Use Discussion Forums4m
Supervised Learning4m
Unsupervised Learning3m
Who are Mentors?3m
Get to Know Your Classmates8m
Frequently Asked Questions11m
Lecture Slides20m
1 ejercicio de práctica
Introduction10m
2 horas para completar

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 videos (Total 70 minutos), 8 readings, 1 quiz
7 videos
Cost Function8m
Cost Function - Intuition I11m
Cost Function - Intuition II8m
Gradient Descent11m
Gradient Descent Intuition11m
Gradient Descent For Linear Regression10m
8 lecturas
Model Representation3m
Cost Function3m
Cost Function - Intuition I4m
Cost Function - Intuition II3m
Gradient Descent3m
Gradient Descent Intuition3m
Gradient Descent For Linear Regression6m
Lecture Slides20m
1 ejercicio de práctica
Linear Regression with One Variable10m
2 horas para completar

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 videos (Total 61 minutos), 7 readings, 1 quiz
6 videos
Addition and Scalar Multiplication6m
Matrix Vector Multiplication13m
Matrix Matrix Multiplication11m
Matrix Multiplication Properties9m
Inverse and Transpose11m
7 lecturas
Matrices and Vectors2m
Addition and Scalar Multiplication3m
Matrix Vector Multiplication2m
Matrix Matrix Multiplication2m
Matrix Multiplication Properties2m
Inverse and Transpose3m
Lecture Slides10m
1 ejercicio de práctica
Linear Algebra10m
Semana
2
3 horas para completar

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 videos (Total 65 minutos), 16 readings, 1 quiz
8 videos
Gradient Descent for Multiple Variables5m
Gradient Descent in Practice I - Feature Scaling8m
Gradient Descent in Practice II - Learning Rate8m
Features and Polynomial Regression7m
Normal Equation16m
Normal Equation Noninvertibility5m
Working on and Submitting Programming Assignments3m
16 lecturas
Setting Up Your Programming Assignment Environment8m
Access MATLAB Online and Upload the Exercise Files3m
Installing Octave on Windows3m
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10m
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3m
Installing Octave on GNU/Linux7m
More Octave/MATLAB resources10m
Multiple Features3m
Gradient Descent For Multiple Variables2m
Gradient Descent in Practice I - Feature Scaling3m
Gradient Descent in Practice II - Learning Rate4m
Features and Polynomial Regression3m
Normal Equation3m
Normal Equation Noninvertibility2m
Programming tips from Mentors10m
Lecture Slides20m
1 ejercicio de práctica
Linear Regression with Multiple Variables10m
5 horas para completar

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 videos (Total 80 minutos), 1 reading, 2 quizzes
6 videos
Moving Data Around16m
Computing on Data13m
Plotting Data9m
Control Statements: for, while, if statement12m
Vectorization13m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Octave/Matlab Tutorial10m
Semana
3
2 horas para completar

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 videos (Total 71 minutos), 8 readings, 1 quiz
7 videos
Hypothesis Representation7m
Decision Boundary14m
Cost Function10m
Simplified Cost Function and Gradient Descent10m
Advanced Optimization14m
Multiclass Classification: One-vs-all6m
8 lecturas
Classification2m
Hypothesis Representation3m
Decision Boundary3m
Cost Function3m
Simplified Cost Function and Gradient Descent3m
Advanced Optimization3m
Multiclass Classification: One-vs-all3m
Lecture Slides10m
1 ejercicio de práctica
Logistic Regression10m
4 horas para completar

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 videos (Total 39 minutos), 5 readings, 2 quizzes
4 videos
Cost Function10m
Regularized Linear Regression10m
Regularized Logistic Regression8m
5 lecturas
The Problem of Overfitting3m
Cost Function3m
Regularized Linear Regression3m
Regularized Logistic Regression3m
Lecture Slides10m
1 ejercicio de práctica
Regularization10m
Semana
4
5 horas para completar

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 videos (Total 63 minutos), 6 readings, 2 quizzes
7 videos
Neurons and the Brain7m
Model Representation I12m
Model Representation II11m
Examples and Intuitions I7m
Examples and Intuitions II10m
Multiclass Classification3m
6 lecturas
Model Representation I6m
Model Representation II6m
Examples and Intuitions I2m
Examples and Intuitions II3m
Multiclass Classification3m
Lecture Slides10m
1 ejercicio de práctica
Neural Networks: Representation10m
Semana
5
5 horas para completar

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.

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8 videos (Total 78 minutos), 8 readings, 2 quizzes
8 videos
Backpropagation Algorithm11m
Backpropagation Intuition12m
Implementation Note: Unrolling Parameters7m
Gradient Checking11m
Random Initialization6m
Putting It Together13m
Autonomous Driving6m
8 lecturas
Cost Function4m
Backpropagation Algorithm10m
Backpropagation Intuition4m
Implementation Note: Unrolling Parameters3m
Gradient Checking3m
Random Initialization3m
Putting It Together4m
Lecture Slides10m
1 ejercicio de práctica
Neural Networks: Learning10m
Semana
6
5 horas para completar

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.

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7 videos (Total 63 minutos), 7 readings, 2 quizzes
7 videos
Evaluating a Hypothesis7m
Model Selection and Train/Validation/Test Sets12m
Diagnosing Bias vs. Variance7m
Regularization and Bias/Variance11m
Learning Curves11m
Deciding What to Do Next Revisited6m
7 lecturas
Evaluating a Hypothesis4m
Model Selection and Train/Validation/Test Sets3m
Diagnosing Bias vs. Variance3m
Regularization and Bias/Variance3m
Learning Curves3m
Deciding What to do Next Revisited3m
Lecture Slides10m
1 ejercicio de práctica
Advice for Applying Machine Learning10m
1 hora para completar

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.

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5 videos (Total 60 minutos), 3 readings, 1 quiz
5 videos
Error Analysis13m
Error Metrics for Skewed Classes11m
Trading Off Precision and Recall14m
Data For Machine Learning11m
3 lecturas
Prioritizing What to Work On3m
Error Analysis3m
Lecture Slides10m
1 ejercicio de práctica
Machine Learning System Design10m
Semana
7
5 horas para completar

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.

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6 videos (Total 98 minutos), 1 reading, 2 quizzes
6 videos
Large Margin Intuition10m
Mathematics Behind Large Margin Classification19m
Kernels I15m
Kernels II15m
Using An SVM21m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Support Vector Machines10m
Semana
8
1 hora para completar

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.

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5 videos (Total 39 minutos), 1 reading, 1 quiz
5 videos
K-Means Algorithm12m
Optimization Objective7m
Random Initialization7m
Choosing the Number of Clusters8m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Unsupervised Learning10m
4 horas para completar

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.

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7 videos (Total 67 minutos), 1 reading, 2 quizzes
7 videos
Motivation II: Visualization5m
Principal Component Analysis Problem Formulation9m
Principal Component Analysis Algorithm15m
Reconstruction from Compressed Representation3m
Choosing the Number of Principal Components10m
Advice for Applying PCA12m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Principal Component Analysis10m
Semana
9
2 horas para completar

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.

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8 videos (Total 91 minutos), 1 reading, 1 quiz
8 videos
Gaussian Distribution10m
Algorithm12m
Developing and Evaluating an Anomaly Detection System13m
Anomaly Detection vs. Supervised Learning7m
Choosing What Features to Use12m
Multivariate Gaussian Distribution13m
Anomaly Detection using the Multivariate Gaussian Distribution14m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Anomaly Detection10m
4 horas para completar

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.

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6 videos (Total 58 minutos), 1 reading, 2 quizzes
6 videos
Content Based Recommendations14m
Collaborative Filtering10m
Collaborative Filtering Algorithm8m
Vectorization: Low Rank Matrix Factorization8m
Implementational Detail: Mean Normalization8m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Recommender Systems10m
Semana
10
1 hora para completar

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.

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6 videos (Total 64 minutos), 1 reading, 1 quiz
6 videos
Stochastic Gradient Descent13m
Mini-Batch Gradient Descent6m
Stochastic Gradient Descent Convergence11m
Online Learning12m
Map Reduce and Data Parallelism14m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Large Scale Machine Learning10m
Semana
11
1 hora para completar

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.

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5 videos (Total 57 minutos), 1 reading, 1 quiz
5 videos
Sliding Windows14m
Getting Lots of Data and Artificial Data16m
Ceiling Analysis: What Part of the Pipeline to Work on Next13m
Summary and Thank You4m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Application: Photo OCR10m
4.9
25,588 revisionesChevron Right

40%

comenzó una nueva carrera después de completar estos cursos

38%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones sobre Aprendizaje Automático

por 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.

por JSJun 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..

Instructor

Avatar

Andrew Ng

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

Acerca de Universidad 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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