Aug 24, 2019
Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.
May 28, 2018
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
교육 기관: Vitalii A•
Dec 10, 2018
Not very related to finance plus most of the tasks are easy to complete, but hard to understand what needs to be done.
교육 기관: Alan X•
Jul 29, 2018
There is always something to be fixed in the assignments... Great content and relevance though.
교육 기관: Ruixin Y•
Jun 18, 2018
Spent more time than expected. And when I tried to access the last assignment, it showed "404 : Not Found You are requesting a page that does not exist!"I understand the professor and other TA put a lot of effort on these courses, but I would say the assignments are not well organized, and more instructions are needed. Really hope the instructors could update/improve the courses/assignments. Thanks.
교육 기관: Desi I•
Sep 18, 2018
Good overview of ML and some basic applications to finance.
The pace is very good for people with some training in statistics and maths.
The assignments, however, are not particularly clear and with some obvious errors. There's room for improvement in the description of the exercises as well as including some tests to verify that you're getting the correct output.
교육 기관: Shobhit L•
Aug 06, 2018
The assignments can improve a lot. The jupyter notebooks have no clarity in instructions and most of the time we have to struggle to find exactly what is expected from our code.
The specialization has a lot of potential, anchored only by the lack of the quality of the assignments.
교육 기관: Gonzalo•
Aug 31, 2018
Great content, but the labs are difficult to understand and often unrelated with the content.
교육 기관: Umendra C•
Nov 18, 2018
Course material is good and a rating of 4 stars or more would have been a fair one, if it was not for very poorly designed and ill prepared assignments. The teaching staff really need to step up a level or two for the assignments.
The course content is good and that the only reason, I am still sticking with this specialization.
교육 기관: Vincent G•
Nov 20, 2018
Content of the class is really good but technology/support is deplorable (Had to wait 3 weeks before the assignments got fixed by the support staff)
교육 기관: Philipp P•
Oct 06, 2018
Cons: overall content is good. Pros: when you release something (software or scientific article) you often do rigorous testing. Why not to do it with your Jupyter Notebooks? I do not understand it.
교육 기관: cyril c•
Oct 11, 2018
content of the lessons is quite good, I would give it 5 stars if the assignments weren't so buggy, contains mistakes, unclear instructions, no help from staff/moderator/instructor, technical issues that are not resolved, etc. a lot of frustration, it just feels like the course was rushed to production and they let the students debug it
교육 기관: Curiosity2016•
Sep 22, 2018
It's a good course but the homework is poorly designed with unclear instructions. Moreover, it's better to get familiar with Python before start this course. The suggested book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" is a very good resource.
교육 기관: Lee H C T•
Sep 23, 2018
some python notebook has bugs, wasting time for me to fix
교육 기관: Masato Y•
Apr 14, 2019
교육 기관: Vincent L•
Aug 25, 2019
extremely hard to follow, but better than when it originally came out. I had signed up after numerous ML courses and tried to skip to the later courses in this specialization. I got stuck trying to implement some crazy equations. I'm ok with looking up api methods, but the need to look out for reshaping is troublesome because it's inconsistent throughout the course. Overall, hard to follow.
교육 기관: Vivek U•
Jul 14, 2018
Exellent content let down by endless flaws in grading system and lack of responses from tutor or instructor. Issues finally resolved 2 days before course end date.
교육 기관: Ricardo F•
Jul 22, 2018
I gave up while working on week 4's homework of the first course of this specialization. The two main reasons that led me to do so are: (1) very little on finance engineering except reference to problem cases and recommended readings; and (2) homework quality is really inferior to other machine learning courses I took at Coursera. I recognize that my first observation may not apply to the remaining courses of this specialization, but it is definitely the case in course 1. In the end, I thought I was not learning enough to justify the time and effort. Lectures are OK but they could be improved a lot by adding more financial engineering elements.
교육 기관: Quentin V•
Jul 29, 2018
The automatic grading system does not work.
교육 기관: Chris M•
Jul 01, 2018
Lectures are good, but assignments are half baked, under specified and half the grading has errors. I hope this improves for people that take (and pay for!) this in the future
교육 기관: Leo s•
Sep 12, 2018
I faced some technique issue with submitting assignment. I hope there would be some technic help.
교육 기관: Conan H•
Sep 27, 2018
Interesting overview let down by lack of clarity on exercises such as the exact formulae and expected format of the outputs.
교육 기관: Omar E O F•
Jun 14, 2019
Very goo lectures, but assessment exercises are not well defined. Examples not shown in lectures. Not enough briefing for starting exercises. No active forum for discussion.
교육 기관: Hrishikesh A R•
Jun 23, 2019
Objectives of assignments are not clear. The instructions provided in assignments are not clear. Tensorflow should be taught extensively because most of the students are facing problems in same.
교육 기관: Amro T•
May 19, 2019
This course is more of mathematical introduction to machine learning than actual practical machine learning tips and tricks course. Math is definitely crucial but the way it was conveyed was not really good. I would have provided a refresher week just in math to refresh the students before jumping into the mathematics in the course. In the notebooks, there is a lot that was missing. Because I was already familiar with the material and I used TensorFlow, Numpy, Sklearn and statsmodels before and built several models with them before, I was able to navigate through. But if I was a totally new student, I would have a very hard time going through those notebooks. A couple of good notes, Please try to summarize all the important equations into a PDF file either for the entire course or per week to be as a reference when needed.
교육 기관: ALI R•
Aug 19, 2019
The course material are presented sparsely despite my initial expectation which may be formed by Andrew Ng in his ML course. Anyway I believe it is a good roadmap for learners of ML in finance and also for me to find and I should be grateful of the Coursera.
교육 기관: Serg D•
Dec 03, 2019
This course is highly academic and has nothing to do with the finance. The only realistic dataset used was for the final project. No resources provided, just names of articles and book chapters. Where am i supposed to get them from? The course does not have the practical part at all. It goes like this: you get 1 hour of videos with formulas and then supposed to write code. HOW????!