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Machine Learning (Stanford University) (coursera.org)

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#1 out of total 134 Machine Learning Tutorials and Courses 13.1k+ views

submitter

Viktor Yanush

1450 points

educator

Andrew Ng

1050 points

Why data enthusiasts like this tutorial?

Content quality

Qualified Instructor

Video quality

Course depth and Coverage

Course Pace

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Kunal Vats

16 points

3 years ago

Any prerequisite to start this course?

Ashwin Jayaprakash

16 Points

@kunal-vats Its explained in the introduction.

Doydle Llama

18 points

4 years ago

Great resource, professor explains things very well, and the assignments are relevant and challenging.

Khushal Sharma

10 points

4 years ago

does this course have programing or only algo's?

Saurabh Hooda

91380 Points

@khushal-sharma This is from the course syllabus: "This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice."

Robert Martin

48 Points

@khushal-sharma It does have programming, but in Octave/Matlab, which nobody really uses for machine learning.

Bingli224

10 points

6 months ago

This course is like to review and upgrade skills of algebra, matrix, vector, calculus, statistics, and MATLAB (like Python + numpy + pandas + matplotlib + Jupyter notebook). Understood the background of machine learning field, especially neural network, as a strong background for studying deep learning.

Rayan Potter

18 points

1 year ago

Machine Learning (ML) field have huge scope in multiple fields to help AI developers train the various types of models and provide an automated solution to different people around the world. Apart from promising sectors like @healthcare, @automotive, @retail and agriculture, ML provides a great opportunity to @machinelearning engineers utilize the bunch of data and train the machine predict in the same way.

Senthil Nathan

10 points

1 year ago

Sahana Ks

12 points

3 years ago

Is it a self paced course?

Saurabh Hooda

91380 Points

@sahana-ks It's not a self paced course. You need to enroll in the course and then for the best output go with the flow of the course. However, once you've enrolled you will have access to all the materials forever so you can access those and revisit those at your pace.