Об этом курсе
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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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Предполагаемая нагрузка: 7 hours/week

Прибл. 53 ч. на завершение
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Субтитры: English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese

Приобретаемые навыки

Machine LearningArtificial Neural NetworkMachine Learning AlgorithmsLogistic Regression
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 7 hours/week

Прибл. 53 ч. на завершение
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English

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

Программа курса: что вы изучите

1

Раздел
Clock
2 ч. на завершение

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 видео (всего 42 мин.), 9 материалов для самостоятельного изучения, 1 тест
Video5 видео
Welcome6мин
What is Machine Learning?7мин
Supervised Learning12мин
Unsupervised Learning14мин
Reading9 материала для самостоятельного изучения
Machine Learning Honor Code8мин
What is Machine Learning?5мин
How to Use Discussion Forums4мин
Supervised Learning4мин
Unsupervised Learning3мин
Who are Mentors?3мин
Get to Know Your Classmates8мин
Frequently Asked Questions11мин
Lecture Slides20мин
Quiz1 практическое упражнение
Introduction10мин
Clock
2 ч. на завершение

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 видео (всего 70 мин.), 8 материалов для самостоятельного изучения, 1 тест
Video7 видео
Cost Function8мин
Cost Function - Intuition I11мин
Cost Function - Intuition II8мин
Gradient Descent11мин
Gradient Descent Intuition11мин
Gradient Descent For Linear Regression10мин
Reading8 материала для самостоятельного изучения
Model Representation3мин
Cost Function3мин
Cost Function - Intuition I4мин
Cost Function - Intuition II3мин
Gradient Descent3мин
Gradient Descent Intuition3мин
Gradient Descent For Linear Regression6мин
Lecture Slides20мин
Quiz1 практическое упражнение
Linear Regression with One Variable10мин
Clock
2 ч. на завершение

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 видео (всего 61 мин.), 7 материалов для самостоятельного изучения, 1 тест
Video6 видео
Addition and Scalar Multiplication6мин
Matrix Vector Multiplication13мин
Matrix Matrix Multiplication11мин
Matrix Multiplication Properties9мин
Inverse and Transpose11мин
Reading7 материала для самостоятельного изучения
Matrices and Vectors2мин
Addition and Scalar Multiplication3мин
Matrix Vector Multiplication2мин
Matrix Matrix Multiplication2мин
Matrix Multiplication Properties2мин
Inverse and Transpose3мин
Lecture Slides10мин
Quiz1 практическое упражнение
Linear Algebra10мин

2

Раздел
Clock
3 ч. на завершение

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 видео (всего 65 мин.), 16 материалов для самостоятельного изучения, 1 тест
Video8 видео
Gradient Descent for Multiple Variables5мин
Gradient Descent in Practice I - Feature Scaling8мин
Gradient Descent in Practice II - Learning Rate8мин
Features and Polynomial Regression7мин
Normal Equation16мин
Normal Equation Noninvertibility5мин
Working on and Submitting Programming Assignments3мин
Reading16 материала для самостоятельного изучения
Setting Up Your Programming Assignment Environment8мин
Accessing MATLAB Online and Uploading the Exercise Files3мин
Installing Octave on Windows3мин
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10мин
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3мин
Installing Octave on GNU/Linux7мин
More Octave/MATLAB resources10мин
Multiple Features3мин
Gradient Descent For Multiple Variables2мин
Gradient Descent in Practice I - Feature Scaling3мин
Gradient Descent in Practice II - Learning Rate4мин
Features and Polynomial Regression3мин
Normal Equation3мин
Normal Equation Noninvertibility2мин
Programming tips from Mentors10мин
Lecture Slides20мин
Quiz1 практическое упражнение
Linear Regression with Multiple Variables10мин
Clock
5 ч. на завершение

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 видео (всего 80 мин.), 1 материал для самостоятельного изучения, 2 тестов
Video6 видео
Moving Data Around16мин
Computing on Data13мин
Plotting Data9мин
Control Statements: for, while, if statement12мин
Vectorization13мин
Reading1 материал для самостоятельного изучения
Lecture Slides10мин
Quiz1 практическое упражнение
Octave/Matlab Tutorial10мин

3

Раздел
Clock
2 ч. на завершение

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 видео (всего 71 мин.), 8 материалов для самостоятельного изучения, 1 тест
Video7 видео
Hypothesis Representation7мин
Decision Boundary14мин
Cost Function10мин
Simplified Cost Function and Gradient Descent10мин
Advanced Optimization14мин
Multiclass Classification: One-vs-all6мин
Reading8 материала для самостоятельного изучения
Classification2мин
Hypothesis Representation3мин
Decision Boundary3мин
Cost Function3мин
Simplified Cost Function and Gradient Descent3мин
Advanced Optimization3мин
Multiclass Classification: One-vs-all3мин
Lecture Slides10мин
Quiz1 практическое упражнение
Logistic Regression10мин
Clock
4 ч. на завершение

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 видео (всего 39 мин.), 5 материалов для самостоятельного изучения, 2 тестов
Video4 видео
Cost Function10мин
Regularized Linear Regression10мин
Regularized Logistic Regression8мин
Reading5 материала для самостоятельного изучения
The Problem of Overfitting3мин
Cost Function3мин
Regularized Linear Regression3мин
Regularized Logistic Regression3мин
Lecture Slides10мин
Quiz1 практическое упражнение
Regularization10мин

4

Раздел
Clock
5 ч. на завершение

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 видео (всего 63 мин.), 6 материалов для самостоятельного изучения, 2 тестов
Video7 видео
Neurons and the Brain7мин
Model Representation I12мин
Model Representation II11мин
Examples and Intuitions I7мин
Examples and Intuitions II10мин
Multiclass Classification3мин
Reading6 материала для самостоятельного изучения
Model Representation I6мин
Model Representation II6мин
Examples and Intuitions I2мин
Examples and Intuitions II3мин
Multiclass Classification3мин
Lecture Slides10мин
Quiz1 практическое упражнение
Neural Networks: Representation10мин
4.9
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получил значимые преимущества в карьере благодаря этому курсу

Лучшие рецензии

автор: 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.

автор: MNOct 31st 2017

Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.

Преподаватель

Andrew Ng

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

О 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....

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  • 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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