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Learner Reviews & Feedback for Machine Learning by Stanford University

167,121 ratings
42,786 reviews

About the Course

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....

Top reviews

Oct 30, 2021

Thank you very much for the excellent lectures. I am just wondering about the back propagation algorithm. When we calculate the errors backward, why do we use matrices theta instead of their inverses.

Aug 19, 2019

It is the best online course for any person wanna learn machine learning. Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful.

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301 - 325 of 10,000 Reviews for Machine Learning

By Joy F Y

Aug 7, 2015

It's very useful


Aug 8, 2021

Andrew Ng yyds!

By Pavel K

Jun 6, 2019

A great course.

By Hacker O

Jun 17, 2019

very good!!

By Stephen M

Jun 5, 2019

Very useful

By ylfgd

Jun 6, 2019

very good

By Thierry L

Jan 4, 2019


By Saiful A

Aug 7, 2015

Very Nice

By Vivek K

Dec 13, 2018


By Nazir A Z

Jul 29, 2021


By Lichen N

Aug 28, 2019


By Sam C

Jan 2, 2020

I'm not crazy about online learning. There are certain aspects of classroom learning that online learning can't give. But as far as online learning goes, this course is probably about as good as it ever gets.

Prof. Ng gives very clear expositions of the fundamentals of machine learning. Anyone taking this class and completing the assignments will be ready to apply machine learning to at least some simpler real world problems and should be in a position to quickly pick up more advanced techniques for more complex problems.

The exams are fair (although I think some more work could have been done to make many of the questions less ambiguous). The programming assignments can be a time sink, but I don't think they could have been any shorter and still give valuable practice in using the techniques outlined in the lectures.

Students who already have a background in linear algebra or the basics of data analysis might find the pace of the class in the early units, where Prof. Ng deals with linear regression, to be rather slow. But if you can get through those early units, you will definitely find yourself dealing with new material (and occasionally appreciating the initial slow pace).

Octave/Matlab is the only language in which the assignments are accepted. I personally would have voted for python. But Prof. Ng spends a few lectures telling you all you need to know about Octave/Matlab, for the purposes of the course. (To save time, I would advise that you spend a day or two learning the language on your own before starting this course. That will allow you to stay that much more ahead of the due dates. But maybe that's just me.)

One word of warning is that, as a friend of mine said after taking a machine learning class in a traditional university classroom, this material makes machine learning accessible, but also takes the "magic" out of it. If you are impressed at how Netflix can be so good at recommending new movies for you to watch, well, after taking this class, you won't be impressed anymore. You'll probably be figuring that, yeah, they probably have some tricks I don't know about, but I could do 90% of what they're doing myself! Which actually means it's a good class!

One thing I definitely would have added are some words at the end of the course about what the "hot topics" are in machine learning, and suggestions about where to go from here, what topics would reward further study, and what books, websites etc. are available for studying them. For example, some words on where to study how and when machine learning turns into full blown artificial intelligence would be appreciated.

The only real gripe I have is that the assignment due dates really didn't give appropriate regard to how busy real life can get during the winter holidays. After all, the big selling point of online learning is flexibility! Right?

In summary: I figure this class is about as good as online learning will get. The instructor is very clear; the assignments are fair and useful. I would have done a few things differently, but nothing is ever perfect. This is a good class for anyone wanting to know the basics of machine learning. Four stars.

By Saideep G

Apr 9, 2019

Very well made, well paced. Better than majority of college courses. Some errors do pop up midway through the course that should be addressed. It can be frustrating to push through these issues sometimes but they are the only thing keeping from 5 stars.

By Cheung C H

Dec 8, 2021

1. better teach in Python

2. sound recording quality can be better

3. Overall content is good

4. please provide more math details, such as in back propagation, and partial derivative is actually very basic where every undergraduate should already know

By Doreen B

Jun 9, 2019

Well explained, at the end of this course you will understand the subject and hold coherent conversations about it. Matlab implementation relatively simple, maybe too much so. Highly recommended course.

By Moto G

Nov 8, 2018

There is a lot to say about you Andrew sir but in few words - "Thank you very much for teaching us the ML concepts in such a beautiful manner "

By Mehdi E F

Mar 19, 2019

Very instructive course.

Thank you.

It would have been great to get an OCR exercice at the end.

By Nils W

Mar 23, 2019

Great course, but the sound quality is quite bad.

By Sai v P

Aug 5, 2019

Better upgrade from matlab to Python

By Alexander S

Jul 17, 2018

I think the rating depends on the expectations.

For a beginner with no prior background with Linear Algebra, statistics, Matlab etc, this is a good overview course (4 stars).

For a professional with prior background, I think this is a poor course (2 stars), because it fails to meet the expectation of learning a deeper understanding of the subject.

The materials covered were with low academic quality (suited for beginner students), any derivations or proofs are omitted if they are non-trivial. The theoretical background created is shallow. I didn't really get a good understanding of the fundamental tools and algorithms used in practical ML solutions, except for the simplest ones.

Yes, the course did give me some better background in ML. But the statement of Mr Ng that the course graduates are now ML experts is highly questionable.

In addition, there is a significant errata for part of the videos & slides - it would be nice to correct them. The fact that about a half of the course material has only slides (which are not always self-explanatory) and not structured course notes is also a point that I recommend improving.

By Stefano B

Sep 10, 2016

Despite I guess the course has a pretty good coverage of the ML basics, it is definitely just an introductive class. In particular I was surprised by the low quality of the material.

The following are my notes and suggestions:

-- I found the lectures highly redundant, with many unnecessary repetitions

-- using a vector notation (like an arrow or a simple line on top of the letters) throughout the course would have make formulas much more readable

-- too much hand writing on the slides while talking: a better set of slides with blocks of text shown at the right moment would be much smoother and readable

-- very, very poor video editing (many times it's clear some parts of the videos were meant to be cut!!)

-- the desire to create a format suitable for people with a scarce algebra preparation lead to use not the appropriate terminology, which would be more correct and easier to understand. Just realize that ML is basically applied math, and without a good math knowledge it is almost pointless to approach the subject

By Eric S

Jun 6, 2018

This course needs to be severely updated and fixed. It is mostly kept alive by the amazing community of mentors, in particular, Tom Mosher. Without Tom, I would have gotten extremely frustrated with the weird quirks that come about during assignments. One important piece of advice: if you can do assignments in an Octave environment such as GNU Octave 4.0.3, I'd strongly recommend it (Althought it tends to crash ofter, so save, save, save!!!).

By Vyacheslav G

Feb 23, 2019

Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave

By Malcomb M

Jul 21, 2017

Content was OK, but quality of teaching was fair at best -- important points glossed over, many not made clear at all, some simply omitted: Bayes classifiers, decision trees, etc, etc.. Audio visual quality of lectures poor. Ng's onscreen scrawls and voice recording were terrible, and there were many mistakes in graphics. Numerous typographical errors in exercise instruction .pdf's. Exercise text itself (ex__.m files) had numerous "pauses" that failed to instruct the user what he had to do (or not do) next, so you had to carefully examine what followed. If more care was put into exercise construction, the "pause" text in the command window would not just say "Enter to continue" but say what coding action was needed to continue. Obviously a lot of work has already been done on interactivity: Quizzes, online Submit scripts, which for me all worked extremely well. But clearly the course could use a lot of improvement in many aspects. Thus I grade it: C-

By Matthew C

May 31, 2019

Dr. Yang does an excellent job explaining concepts and showing the detailed mechanics of any example he brings up. This being said, I felt the course offered more of an overview, and for anyone with a college statistics and programming course, this won't be very useful, frankly. The course didn't provide lots of new information, and I think much of the actual theory and implementation for ML and its applications would be better broken up into a series of more rigorous courses. This would however, be a good fit for someone working in management who needs a quick understanding of the most basic principles of ML.