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#### English

Subtitles: Chinese (Simplified), English, Hebrew, Spanish, Hindi, Japanese...
Learners taking this Course are
• Software Engineers
• Machine Learning Engineers
• Risk Managers
• Chief Technology Officers (CTOs)

### Skills you will gain

Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning
Learners taking this Course are
• Software Engineers
• Machine Learning Engineers
• Risk Managers
• Chief Technology Officers (CTOs)

#### 100% online

Start instantly and learn at your own schedule.

#### English

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

### 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
Welcome6m
What is Machine Learning?7m
Supervised Learning12m
Unsupervised Learning14m
Machine Learning Honor Code8m
What is Machine Learning?5m
How to Use Discussion Forums4m
Supervised Learning4m
Unsupervised Learning3m
Who are Mentors?3m
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 Function8m
Cost Function - Intuition I11m
Cost Function - Intuition II8m
Model Representation3m
Cost Function3m
Cost Function - Intuition I4m
Cost Function - Intuition II3m
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 Vector Multiplication13m
Matrix Matrix Multiplication11m
Matrix Multiplication Properties9m
Inverse and Transpose11m
Matrices and Vectors2m
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 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
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 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
Moving Data Around16m
Computing on Data13m
Plotting Data9m
Control Statements: for, while, if statement12m
Vectorization13m
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
Hypothesis Representation7m
Decision Boundary14m
Cost Function10m
Simplified Cost Function and Gradient Descent10m
Multiclass Classification: One-vs-all6m
Classification2m
Hypothesis Representation3m
Decision Boundary3m
Cost Function3m
Simplified Cost Function and Gradient Descent3m
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
Cost Function10m
Regularized Linear Regression10m
Regularized Logistic Regression8m
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
Neurons and the Brain7m
Model Representation I12m
Model Representation II11m
Examples and Intuitions I7m
Examples and Intuitions II10m
Multiclass Classification3m
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
28701 Reviews

## 40%

started a new career after completing these courses

## 38%

got a tangible career benefit from this course

### Top reviews from Machine Learning

By SSMay 17th 2019

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

By VBOct 3rd 2016

Everything is great about this course. Dr. Ng dumbs is it down with the complex math involved. He explained everything clearly, slowly and softly. Now I can say I know something about Machine Learning

## Instructor

### Andrew Ng

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