About this Course
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Approx. 56 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. 56 hours to complete

English

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

Learners taking this Course are

  • Technical Leads
  • Machine Learning Engineers
  • Software Engineers
  • Risk Managers
  • Chief Technology Officers (CTOs)

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Introduction

5 videos (Total 42 min), 9 readings, 1 quiz
5 videos
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

7 videos (Total 70 min), 8 readings, 1 quiz
7 videos
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

6 videos (Total 61 min), 7 readings, 1 quiz
6 videos
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

8 videos (Total 65 min), 16 readings, 1 quiz
8 videos
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 the Updated 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

6 videos (Total 80 min), 1 reading, 2 quizzes
6 videos
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

7 videos (Total 71 min), 8 readings, 1 quiz
7 videos
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

4 videos (Total 39 min), 5 readings, 2 quizzes
4 videos
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

7 videos (Total 63 min), 6 readings, 2 quizzes
7 videos
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 from Machine Learning

By RCJul 19th 2019

Amazing course. It gets deep into the content and now I feel I know at least the basics of Machine Learning. This is definitely going to help me on my job! Thanks Andrew and the mentors of the course!

By 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.

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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