oferecido por

Stanford University

Informações sobre o curso

4.9

82,433 ratings

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21,318 reviews

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....

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Aprox. 53 horas restantes

Legendas: English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese

Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Aprox. 53 horas restantes

Legendas: English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese

Seção

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

Welcome6min

What is Machine Learning?7min

Supervised Learning12min

Unsupervised Learning14min

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

Introduction10min

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

Cost Function8min

Cost Function - Intuition I11min

Cost Function - Intuition II8min

Gradient Descent11min

Gradient Descent Intuition11min

Gradient Descent For Linear Regression10min

Model Representation3min

Cost Function3min

Cost Function - Intuition I4min

Cost Function - Intuition II3min

Gradient Descent3min

Gradient Descent Intuition3min

Gradient Descent For Linear Regression6min

Lecture Slides20min

Linear Regression with One Variable10min

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

Addition and Scalar Multiplication6min

Matrix Vector Multiplication13min

Matrix Matrix Multiplication11min

Matrix Multiplication Properties9min

Inverse and Transpose11min

Matrices and Vectors2min

Addition and Scalar Multiplication3min

Matrix Vector Multiplication2min

Matrix Matrix Multiplication2min

Matrix Multiplication Properties2min

Inverse and Transpose3min

Lecture Slides10min

Linear Algebra10min

Seção

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

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

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

Linear Regression with Multiple Variables10min

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

Basic Operations13min

Moving Data Around16min

Computing on Data13min

Plotting Data9min

Control Statements: for, while, if statement12min

Vectorization13min

Lecture Slides10min

Octave/Matlab Tutorial10min

Seção

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

Classification8min

Hypothesis Representation7min

Decision Boundary14min

Cost Function10min

Simplified Cost Function and Gradient Descent10min

Advanced Optimization14min

Multiclass Classification: One-vs-all6min

Classification2min

Hypothesis Representation3min

Decision Boundary3min

Cost Function3min

Simplified Cost Function and Gradient Descent3min

Advanced Optimization3min

Multiclass Classification: One-vs-all3min

Lecture Slides10min

Logistic Regression10min

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

Cost Function10min

Regularized Linear Regression10min

Regularized Logistic Regression8min

The Problem of Overfitting3min

Cost Function3min

Regularized Linear Regression3min

Regularized Logistic Regression3min

Lecture Slides10min

Regularization10min

Seção

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

Neurons and the Brain7min

Model Representation I12min

Model Representation II11min

Examples and Intuitions I7min

Examples and Intuitions II10min

Multiclass Classification3min

Model Representation I6min

Model Representation II6min

Examples and Intuitions I2min

Examples and Intuitions II3min

Multiclass Classification3min

Lecture Slides10min

Neural Networks: Representation10min

4.9

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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.

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....

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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