About this Course

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Learner Career Outcomes

40%

started a new career after completing these courses

38%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 60 hours to complete
English

Skills you will gain

Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

Learner Career Outcomes

40%

started a new career after completing these courses

38%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 60 hours to complete
English

Offered by

Placeholder

Stanford University

Syllabus - What you will learn from this course

Content RatingThumbs Up97%(1,271,035 ratings)Info
Week
1

Week 1

2 hours to complete

Introduction

2 hours to complete
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
Introduction30m
2 hours to complete

Linear Regression with One Variable

2 hours to complete
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 Variable30m
2 hours to complete

Linear Algebra Review

2 hours to complete
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 Algebra30m
Week
2

Week 2

3 hours to complete

Linear Regression with Multiple Variables

3 hours to complete
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 to MATLAB Online and the Exercise Files for MATLAB Users3m
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 Variables30m
5 hours to complete

Octave/Matlab Tutorial

5 hours to complete
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 Tutorial30m
Week
3

Week 3

2 hours to complete

Logistic Regression

2 hours to complete
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 Regression30m
5 hours to complete

Regularization

5 hours to complete
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
Regularization30m
Week
4

Week 4

5 hours to complete

Neural Networks: Representation

5 hours to complete
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: Representation30m

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