Machine Learning
ofrecido por
Machine Learning
Stanford University
Acerca de este Curso
4.9
84,254 calificaciones
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.
Habilidades que obtendrás
Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning
Programa - Qué aprenderás en este curso
Week
1Introduction
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 videos (Total: 42 min), 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
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 videos (Total: 70 min), 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
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 videos (Total: 61 min), 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
Week
2Linear 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 videos (Total: 65 min), 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
Accessing MATLAB Online and Uploading 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
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 videos (Total: 80 min), 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
Week
3Logistic 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 min), 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
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 videos (Total: 39 min), 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
Week
4Neural 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 videos (Total: 63 min), 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
4.9
39%
83%
Principales revisiones
por VB•Oct 3rd 2016
Everything is great about this course. Dr. Ng dumbs is it down with the complex math involved. He explained everything clearly, slowly and softly. Now I can say I know something about Machine Learning
por AQ•Mar 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.
Instructor
Andrew Ng
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google BrainAcerca de 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.
Preguntas Frecuentes
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?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
Coursera brinda acceso universal a la mejor educación del mundo, al asociarse con las mejores universidades y organizaciones, para ofrecer cursos en línea.
© 2018 Coursera Inc. Todos los derechos reservados.
