Machine Learning
oferecido por
Machine Learning
Stanford University
Informações sobre o curso
4.9
82,433 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.
Habilidades que você terá
Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning
Programa - O que você aprenderá com este curso
1
Seção
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 vídeos (Total de 42 min), 9 leituras, 1 teste
5 videos
Welcome6min
What is Machine Learning?7min
Supervised Learning12min
Unsupervised Learning14min
9 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
1 exercício prático
Introduction10min
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 vídeos (Total de 70 min), 8 leituras, 1 teste
7 videos
Cost Function8min
Cost Function - Intuition I11min
Cost Function - Intuition II8min
Gradient Descent11min
Gradient Descent Intuition11min
Gradient Descent For Linear Regression10min
8 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
1 exercício prático
Linear Regression with One Variable10min
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 vídeos (Total de 61 min), 7 leituras, 1 teste
6 videos
Addition and Scalar Multiplication6min
Matrix Vector Multiplication13min
Matrix Matrix Multiplication11min
Matrix Multiplication Properties9min
Inverse and Transpose11min
7 leituras
Matrices and Vectors2min
Addition and Scalar Multiplication3min
Matrix Vector Multiplication2min
Matrix Matrix Multiplication2min
Matrix Multiplication Properties2min
Inverse and Transpose3min
Lecture Slides10min
1 exercício prático
Linear Algebra10min
2
Seção
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 vídeos (Total de 65 min), 16 leituras, 1 teste
8 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
16 leituras
Setting Up Your Programming Assignment Environment8min
Accessing MATLAB Online and Uploading 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
1 exercício prático
Linear Regression with Multiple Variables10min
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 vídeos (Total de 80 min), 1 leitura, 2 testes
6 videos
Basic Operations13min
Moving Data Around16min
Computing on Data13min
Plotting Data9min
Control Statements: for, while, if statement12min
Vectorization13min
1 leituras
Lecture Slides10min
1 exercício prático
Octave/Matlab Tutorial10min
3
Seção
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 vídeos (Total de 71 min), 8 leituras, 1 teste
7 videos
Classification8min
Hypothesis Representation7min
Decision Boundary14min
Cost Function10min
Simplified Cost Function and Gradient Descent10min
Advanced Optimization14min
Multiclass Classification: One-vs-all6min
8 leituras
Classification2min
Hypothesis Representation3min
Decision Boundary3min
Cost Function3min
Simplified Cost Function and Gradient Descent3min
Advanced Optimization3min
Multiclass Classification: One-vs-all3min
Lecture Slides10min
1 exercício prático
Logistic Regression10min
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 vídeos (Total de 39 min), 5 leituras, 2 testes
4 videos
Cost Function10min
Regularized Linear Regression10min
Regularized Logistic Regression8min
5 leituras
The Problem of Overfitting3min
Cost Function3min
Regularized Linear Regression3min
Regularized Logistic Regression3min
Lecture Slides10min
1 exercício prático
Regularization10min
4
Seção
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 vídeos (Total de 63 min), 6 leituras, 2 testes
7 videos
Neurons and the Brain7min
Model Representation I12min
Model Representation II11min
Examples and Intuitions I7min
Examples and Intuitions II10min
Multiclass Classification3min
6 leituras
Model Representation I6min
Model Representation II6min
Examples and Intuitions I2min
Examples and Intuitions II3min
Multiclass Classification3min
Lecture Slides10min
1 exercício prático
Neural Networks: Representation10min
4.9
39%
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Melhores avaliações
por DW•Feb 20th 2016
Fantastic intro to the fundamentals of machine learning. If you want to take your understanding of machine learning concepts beyond "model.fit(X, Y), model.predict(X)" then this is the course for you.
por MM•Jul 8th 2018
Great course! Learned lots of stuffs about ML. I think the programming exercises and the quizzes are efficient way to me to master this course, just watching videos without any practice benefits less.
Instrutores
Andrew Ng
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google BrainSobre 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.
Perguntas Frequentes – FAQ
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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