About this Course
4.9
99,742 ratings
24,929 reviews

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Approx. 55 hours to complete

English

Subtitles: Chinese (Simplified), English, Hebrew, Spanish, Hindi, Japanese...

Skills you will gain

Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 55 hours to complete

English

Subtitles: Chinese (Simplified), English, Hebrew, Spanish, Hindi, Japanese...

Syllabus - What you will learn from this course

Week
1
2 hours to complete

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 videos (Total 42 min), 9 readings, 1 quiz
5 videos
Welcome6m
What is Machine Learning?7m
Supervised Learning12m
Unsupervised Learning14m
9 readings
Machine Learning Honor Code8m
What is Machine Learning?5m
How to Use Discussion Forums4m
Supervised Learning4m
Unsupervised Learning3m
Who are Mentors?3m
Get to Know Your Classmates8m
Frequently Asked Questions11m
Lecture Slides20m
1 practice exercise
Introduction10m
2 hours to complete

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 videos (Total 70 min), 8 readings, 1 quiz
7 videos
Cost Function8m
Cost Function - Intuition I11m
Cost Function - Intuition II8m
Gradient Descent11m
Gradient Descent Intuition11m
Gradient Descent For Linear Regression10m
8 readings
Model Representation3m
Cost Function3m
Cost Function - Intuition I4m
Cost Function - Intuition II3m
Gradient Descent3m
Gradient Descent Intuition3m
Gradient Descent For Linear Regression6m
Lecture Slides20m
1 practice exercise
Linear Regression with One Variable10m
2 hours to complete

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 videos (Total 61 min), 7 readings, 1 quiz
6 videos
Addition and Scalar Multiplication6m
Matrix Vector Multiplication13m
Matrix Matrix Multiplication11m
Matrix Multiplication Properties9m
Inverse and Transpose11m
7 readings
Matrices and Vectors2m
Addition and Scalar Multiplication3m
Matrix Vector Multiplication2m
Matrix Matrix Multiplication2m
Matrix Multiplication Properties2m
Inverse and Transpose3m
Lecture Slides10m
1 practice exercise
Linear Algebra10m
Week
2
3 hours to complete

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 videos (Total 65 min), 16 readings, 1 quiz
8 videos
Gradient Descent for Multiple Variables5m
Gradient Descent in Practice I - Feature Scaling8m
Gradient Descent in Practice II - Learning Rate8m
Features and Polynomial Regression7m
Normal Equation16m
Normal Equation Noninvertibility5m
Working on and Submitting Programming Assignments3m
16 readings
Setting Up Your Programming Assignment Environment8m
Access MATLAB Online and Upload the Exercise Files3m
Installing Octave on Windows3m
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10m
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3m
Installing Octave on GNU/Linux7m
More Octave/MATLAB resources10m
Multiple Features3m
Gradient Descent For Multiple Variables2m
Gradient Descent in Practice I - Feature Scaling3m
Gradient Descent in Practice II - Learning Rate4m
Features and Polynomial Regression3m
Normal Equation3m
Normal Equation Noninvertibility2m
Programming tips from Mentors10m
Lecture Slides20m
1 practice exercise
Linear Regression with Multiple Variables10m
5 hours to complete

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 videos (Total 80 min), 1 reading, 2 quizzes
6 videos
Moving Data Around16m
Computing on Data13m
Plotting Data9m
Control Statements: for, while, if statement12m
Vectorization13m
1 reading
Lecture Slides10m
1 practice exercise
Octave/Matlab Tutorial10m
Week
3
2 hours to complete

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 videos (Total 71 min), 8 readings, 1 quiz
7 videos
Hypothesis Representation7m
Decision Boundary14m
Cost Function10m
Simplified Cost Function and Gradient Descent10m
Advanced Optimization14m
Multiclass Classification: One-vs-all6m
8 readings
Classification2m
Hypothesis Representation3m
Decision Boundary3m
Cost Function3m
Simplified Cost Function and Gradient Descent3m
Advanced Optimization3m
Multiclass Classification: One-vs-all3m
Lecture Slides10m
1 practice exercise
Logistic Regression10m
4 hours to complete

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 videos (Total 39 min), 5 readings, 2 quizzes
4 videos
Cost Function10m
Regularized Linear Regression10m
Regularized Logistic Regression8m
5 readings
The Problem of Overfitting3m
Cost Function3m
Regularized Linear Regression3m
Regularized Logistic Regression3m
Lecture Slides10m
1 practice exercise
Regularization10m
Week
4
5 hours to complete

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 videos (Total 63 min), 6 readings, 2 quizzes
7 videos
Neurons and the Brain7m
Model Representation I12m
Model Representation II11m
Examples and Intuitions I7m
Examples and Intuitions II10m
Multiclass Classification3m
6 readings
Model Representation I6m
Model Representation II6m
Examples and Intuitions I2m
Examples and Intuitions II3m
Multiclass Classification3m
Lecture Slides10m
1 practice exercise
Neural Networks: Representation10m
4.9
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Top Reviews

By MNJun 15th 2016

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

By OKApr 18th 2018

You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

Instructor

Avatar

Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain

About 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....

Frequently Asked 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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