À propos de ce cours
4.9
79,139 ratings
20,643 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....
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Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
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Dates limites flexibles

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Recommandé : 7 hours/week

Approx. 53 heures pour terminer
Comment Dots

English

Sous-titres : English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese

Compétences que vous acquerrez

Machine LearningArtificial Neural NetworkMachine Learning AlgorithmsLogistic Regression
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Clock

Recommandé : 7 hours/week

Approx. 53 heures pour terminer
Comment Dots

English

Sous-titres : English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese

Programme du cours : ce que vous apprendrez dans ce cours

1

Section
Clock
2 heures pour terminer

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 vidéos (Total 42 min), 9 lectures, 1 quiz
Video5 vidéos
Welcome6 min
What is Machine Learning?7 min
Supervised Learning12 min
Unsupervised Learning14 min
Reading9 lectures
Machine Learning Honor Code8 min
What is Machine Learning?5 min
How to Use Discussion Forums4 min
Supervised Learning4 min
Unsupervised Learning3 min
Who are Mentors?3 min
Get to Know Your Classmates8 min
Frequently Asked Questions11 min
Lecture Slides20 min
Quiz1 exercice pour s'entraîner
Introduction10 min
Clock
2 heures pour terminer

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 vidéos (Total 70 min), 8 lectures, 1 quiz
Video7 vidéos
Cost Function8 min
Cost Function - Intuition I11 min
Cost Function - Intuition II8 min
Gradient Descent11 min
Gradient Descent Intuition11 min
Gradient Descent For Linear Regression10 min
Reading8 lectures
Model Representation3 min
Cost Function3 min
Cost Function - Intuition I4 min
Cost Function - Intuition II3 min
Gradient Descent3 min
Gradient Descent Intuition3 min
Gradient Descent For Linear Regression6 min
Lecture Slides20 min
Quiz1 exercice pour s'entraîner
Linear Regression with One Variable10 min
Clock
2 heures pour terminer

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 vidéos (Total 61 min), 7 lectures, 1 quiz
Video6 vidéos
Addition and Scalar Multiplication6 min
Matrix Vector Multiplication13 min
Matrix Matrix Multiplication11 min
Matrix Multiplication Properties9 min
Inverse and Transpose11 min
Reading7 lectures
Matrices and Vectors2 min
Addition and Scalar Multiplication3 min
Matrix Vector Multiplication2 min
Matrix Matrix Multiplication2 min
Matrix Multiplication Properties2 min
Inverse and Transpose3 min
Lecture Slides10 min
Quiz1 exercice pour s'entraîner
Linear Algebra10 min

2

Section
Clock
3 heures pour terminer

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 vidéos (Total 65 min), 16 lectures, 1 quiz
Video8 vidéos
Gradient Descent for Multiple Variables5 min
Gradient Descent in Practice I - Feature Scaling8 min
Gradient Descent in Practice II - Learning Rate8 min
Features and Polynomial Regression7 min
Normal Equation16 min
Normal Equation Noninvertibility5 min
Working on and Submitting Programming Assignments3 min
Reading16 lectures
Setting Up Your Programming Assignment Environment8 min
Accessing MATLAB Online and Uploading the Exercise Files3 min
Installing Octave on Windows3 min
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10 min
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3 min
Installing Octave on GNU/Linux7 min
More Octave/MATLAB resources10 min
Multiple Features3 min
Gradient Descent For Multiple Variables2 min
Gradient Descent in Practice I - Feature Scaling3 min
Gradient Descent in Practice II - Learning Rate4 min
Features and Polynomial Regression3 min
Normal Equation3 min
Normal Equation Noninvertibility2 min
Programming tips from Mentors10 min
Lecture Slides20 min
Quiz1 exercice pour s'entraîner
Linear Regression with Multiple Variables10 min
Clock
5 heures pour terminer

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 vidéos (Total 80 min), 1 lecture, 2 quiz
Video6 vidéos
Moving Data Around16 min
Computing on Data13 min
Plotting Data9 min
Control Statements: for, while, if statement12 min
Vectorization13 min
Reading1 lecture
Lecture Slides10 min
Quiz1 exercice pour s'entraîner
Octave/Matlab Tutorial10 min

3

Section
Clock
2 heures pour terminer

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 vidéos (Total 71 min), 8 lectures, 1 quiz
Video7 vidéos
Hypothesis Representation7 min
Decision Boundary14 min
Cost Function10 min
Simplified Cost Function and Gradient Descent10 min
Advanced Optimization14 min
Multiclass Classification: One-vs-all6 min
Reading8 lectures
Classification2 min
Hypothesis Representation3 min
Decision Boundary3 min
Cost Function3 min
Simplified Cost Function and Gradient Descent3 min
Advanced Optimization3 min
Multiclass Classification: One-vs-all3 min
Lecture Slides10 min
Quiz1 exercice pour s'entraîner
Logistic Regression10 min
Clock
4 heures pour terminer

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 vidéos (Total 39 min), 5 lectures, 2 quiz
Video4 vidéos
Cost Function10 min
Regularized Linear Regression10 min
Regularized Logistic Regression8 min
Reading5 lectures
The Problem of Overfitting3 min
Cost Function3 min
Regularized Linear Regression3 min
Regularized Logistic Regression3 min
Lecture Slides10 min
Quiz1 exercice pour s'entraîner
Regularization10 min

4

Section
Clock
5 heures pour terminer

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 vidéos (Total 63 min), 6 lectures, 2 quiz
Video7 vidéos
Neurons and the Brain7 min
Model Representation I12 min
Model Representation II11 min
Examples and Intuitions I7 min
Examples and Intuitions II10 min
Multiclass Classification3 min
Reading6 lectures
Model Representation I6 min
Model Representation II6 min
Examples and Intuitions I2 min
Examples and Intuitions II3 min
Multiclass Classification3 min
Lecture Slides10 min
Quiz1 exercice pour s'entraîner
Neural Networks: Representation10 min
4.9
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a commencé une nouvelle carrière après avoir terminé ces cours
Briefcase

83%

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Meilleurs avis

par JPOct 25th 2016

Great course. A progressive discovery of the maths inner to the learning algorithms. This course gives that insight many ML practitioners don't have and is so important for making real use cases work.

par HSMar 3rd 2018

My first and the most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course. Thanks Andrew Ng and Coursera for this amazing course.

Enseignant

Andrew Ng

Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

À propos 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....

Foire Aux Questions

  • 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.

  • 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.

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