Mar 03, 2018
My first and the most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course. Thanks Andrew Ng and Coursera for this amazing course.
Oct 26, 2017
Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts
автор: Jerome T•
Mar 06, 2019
I like the course very much. One point where it could be improved are the assignments: it is really nice to be guided and to have a big part of the programming prepared but the drawback is that many times I didn't feel in control of what was happening. For example, that was hard to know basic features of the implementation (is this data a row vector? a column vector?) since I didn't decide it. This leads me to spend quite some time on trying to fix simple problems. In short, I wish I had felt more "empowered" during the assignments.
автор: Saideep G•
Apr 09, 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.
автор: MAHESH Y•
Apr 09, 2019
it is one of the best course for beginners in machine learning, the only thing it lacks is its python implementation. If there is the python implementation of this course then no other course is better than this one
автор: Doreen B•
Jun 09, 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.
автор: Mohd F•
Nov 08, 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 "
автор: Mehdi E F•
Mar 19, 2019
Very instructive course.
It would have been great to get an OCR exercice at the end.
автор: Nils W•
Mar 23, 2019
Great course, but the sound quality is quite bad.
автор: Sai V P•
Aug 05, 2019
Better upgrade from matlab to Python
автор: Jerome P•
Mar 30, 2018
Good introduction course, giving an overview of machine learning algorithms and some methodology. Off course a lot can be added, but it's a good start for people with little to no knowledge or experience in this field. A few points that could be improved: I would like to have better material support for each section. Marked-up slides are not a great support for reviewing the different sections afterwards.
It would not hurt to provide a little bit more theoretical background and justification when covering the different algorithms. Andrew Ng almost apologizes when going into mathematical equations, but this is fundamental to machine learning.
quiz assignments are rather easy. They could be a little more challenging
I would rather have the programming assignment using R or python than Matlab.
But still a decent course overall I think.
автор: Eric S•
Jun 06, 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!!!).
автор: Mirko J R•
Apr 02, 2019
Excellent lessons by Prof. Andrew Ng.
However very poor support. No answers from any mentor along lessons, you should resolve all doubts by yourself.
I had a problem with my ID verification, I was waiting for a long time without any responses.
Also, it's difficult to contact persons who could support you, I tried to contact someone but just found a Bot. Terrible support.
автор: Shitai Z•
Nov 19, 2018
Too easy for people with background in machine learning. But would be a good introductory one if you have zero understanding in machine learning and want to change your career track.
автор: Vyacheslav G•
Feb 23, 2019
Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave
Feb 19, 2018
The course is not for people with not mathematical backgrounds plus its using matlab.. these days R and Python are more used in the industry for ML. I found to this course via friends that said it's hard but very recommended.. i think there are easier courses online that can deliver the same concepts
May 11, 2018
Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.
автор: Loftur e•
Sep 17, 2018
Assignments are very messy.
автор: Ivan Č•
Feb 24, 2016
Certificate is expensive!
автор: Hu L•
Feb 14, 2018
Too easy and too slow
автор: Ross K•
Oct 10, 2015
The course is more an exercise in flexing Ivy vernacular than it is actually teaching. The learning curve is too steep to be useful to the majority of potential registrants. You're interested in this course either to (a) learn something about an exciting and ever changing field and/or (b) to have the Stanford logo on your LinkedIn profile. In both cases, move on. The curve is far too steep to be useful or to merit the countless additional hours of background learning the course should have done to bridge the gap.
автор: Larry C•
Feb 24, 2016
There are too many mistakes and misleading statements made in the course material. There were a lot difficulties with submitting assignments in order to move forward in the course. I had to give up because I don't have time to be bogged down like this.
The students' comments and discussion would be useful if they can be accessed from within each lesson. I can't make heads or tails of what the discussions were referring to, when they are all clumped together at the course web site instead.
автор: Alex W•
Dec 14, 2015
The exercises lead you to the edge of a cliff, then push you off. No guidance. Good luck if you don't already know linear algebra, matrix math, and matlab. I'll be looking elsewhere to learn about Machine Learning. Glad I didn't pay for this course!
автор: omri g•
Nov 11, 2015
Been asked to re-take all assignments *after* paying for a certificate! I wil never pay for a Coursera course again, and I would not recommend my friends to do so
автор: Andy M•
Sep 08, 2018
Huge amounts of assumed understanding make this course impenetrable.
автор: Subham B•
Aug 30, 2019
This course is definitely not for beginners.
автор: Harry E•
Oct 04, 2017
Before I go into why I liked this course so much, let me give a little context on my motivation to taking it. My background is a Bachelors in Math, and 9 years working in finance in a role involving very little computer science or statistics. I wanted a change of industries into the world of Data, for which a significant amount of learning and retraining were necessary; however before just enrolling on and committing to a masters degree, I wanted to answer some questions. Do I enjoy this? Am I able to learn it? Do I want to take this field a step further? Fortunately, the answer to all of my questions was positive.
I have to compare this to the ML module of JHU's Data Science specialisation, which I found rather frustrating as it was too brief to properly go into how the algorithms work. No discredit to the JHU team, I thought the overall course was great and served its purpose, but if you are like me and want to understand what's going on under the hood of these algorithms, this is a superb course. None of the maths is particularly hard, you will need to brush up on some linear algebra, and no prior Matlab is required. Some pretty tough concepts are built up from great simple motivating examples, for me the Neural Network / logic function was the best example of this, and I was extremely satisfied with how I grasped the material. There are enough real world applications thrown in to stay relevant (Data Science is a practical field after all), my favourite was seeing my predictions for number recognition appearing on the screen from the Neural Network I'd just trained appear on screen.
One critique I read of the course which I slightly sympathise with is that the programming assignments become a little like syntax exercises coding an equation into Octave, and thus lose their effectiveness in teaching you. I slightly agree with this and would love to have developed more parts of the algorithms myself, but with the limited time the course has, reading through the code of each of the exercises rather than just clicking through is a decent enough half way step. I would recommend everyone to do this, the point of the course is not just to pass the assignments, but to read around the material a little bit and follow exactly what's going on. That has to be left up to the student.
Overall, I feel like I'm equipped with what I need to get my hands dirty with some datasets to work on my own projects, and give Kaggle a crack. And that's pretty cool considering a few weeks ago I knew pretty much nothing about any of this. Onto the next step in my Data journey!