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Machine Learning

Stanford University

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....

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Предполагаемая нагрузка: 7 hours/week

Прибл. 53 ч. на завершение

English

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

Приобретаемые навыки

Machine LearningArtificial Neural NetworkMachine Learning AlgorithmsLogistic Regression

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Предполагаемая нагрузка: 7 hours/week

Прибл. 53 ч. на завершение

English

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

Программа курса: что вы изучите

1

Раздел

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....

5 видео (всего 42 мин.), 9 материалов для самостоятельного изучения, 1 тест

5 видео

Welcome to Machine Learning!1мин

Welcome6мин

What is Machine Learning?7мин

Supervised Learning12мин

Unsupervised Learning14мин

9 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

1 практическое упражнение

Introduction10мин

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....

7 видео (всего 70 мин.), 8 материалов для самостоятельного изучения, 1 тест

7 видео

Model Representation8мин

Cost Function8мин

Cost Function - Intuition I11мин

Cost Function - Intuition II8мин

Gradient Descent11мин

Gradient Descent Intuition11мин

Gradient Descent For Linear Regression10мин

8 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

1 практическое упражнение

Linear Regression with One Variable10мин

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....

6 видео (всего 61 мин.), 7 материалов для самостоятельного изучения, 1 тест

6 видео

Matrices and Vectors8мин

Addition and Scalar Multiplication6мин

Matrix Vector Multiplication13мин

Matrix Matrix Multiplication11мин

Matrix Multiplication Properties9мин

Inverse and Transpose11мин

7 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

1 практическое упражнение

Linear Algebra10мин

2

Раздел

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....

8 видео (всего 65 мин.), 16 материалов для самостоятельного изучения, 1 тест

8 видео

Multiple Features8мин

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

16 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

1 практическое упражнение

Linear Regression with Multiple Variables10мин

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....

6 видео (всего 80 мин.), 1 материал для самостоятельного изучения, 2 тестов

6 видео

Basic Operations13мин

Moving Data Around16мин

Computing on Data13мин

Plotting Data9мин

Control Statements: for, while, if statement12мин

Vectorization13мин

1 материал для самостоятельного изучения

Lecture Slides10мин

1 практическое упражнение

Octave/Matlab Tutorial10мин

3

Раздел

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. ...

7 видео (всего 71 мин.), 8 материалов для самостоятельного изучения, 1 тест

7 видео

Classification8мин

Hypothesis Representation7мин

Decision Boundary14мин

Cost Function10мин

Simplified Cost Function and Gradient Descent10мин

Advanced Optimization14мин

Multiclass Classification: One-vs-all6мин

8 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

1 практическое упражнение

Logistic Regression10мин

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. ...

4 видео (всего 39 мин.), 5 материалов для самостоятельного изучения, 2 тестов

4 видео

The Problem of Overfitting9мин

Cost Function10мин

Regularized Linear Regression10мин

Regularized Logistic Regression8мин

5 материала для самостоятельного изучения

The Problem of Overfitting3мин

Cost Function3мин

Regularized Linear Regression3мин

Regularized Logistic Regression3мин

Lecture Slides10мин

1 практическое упражнение

Regularization10мин

4

Раздел

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. ...

7 видео (всего 63 мин.), 6 материалов для самостоятельного изучения, 2 тестов

7 видео

Non-linear Hypotheses9мин

Neurons and the Brain7мин

Model Representation I12мин

Model Representation II11мин

Examples and Intuitions I7мин

Examples and Intuitions II10мин

Multiclass Classification3мин

6 материала для самостоятельного изучения

Model Representation I6мин

Model Representation II6мин

Examples and Intuitions I2мин

Examples and Intuitions II3мин

Multiclass Classification3мин

Lecture Slides10мин

1 практическое упражнение

Neural Networks: Representation10мин

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....

Часто задаваемые вопросы

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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Machine Learning