I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.
The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.
創建者 vatsal n k•
Overall the course was very good and I love the peer-graded assignment concept. As after completing your assignment you can see other's assignments, there you can point out where you are better than others and where you lack.
One thing to be noted is that the algorithm training part totally in the practice session. So you have to first read/understand the code by yourself then you can implement it. I think the course could be better if video lectures where there for algorithm training part.
創建者 Nandivada P E•
we learned a lot beyond this course.It really explained the Machine learning from basic to the intermediate level and also huge coverage of techniques in python
創建者 Rajdeep S•
Concise presentation,brief and to-the -point explanations, great course for an intermediate ML developer looking to brush up their skills.Programming exercises should me more detailed.
I liked the concept of peer graded final project allowing us to review the projects of other learners as well.
創建者 Pamela W•
I enjoyed this course and thought it was a good high level overview of machine learning. I appreciated the exposure to Jupyter notebooks, but the coursework could have been more python programming focused. There was not much learning of the python language in the course.
創建者 Serhan Ç•
somewhat superficial. I think the course name should be only machine learning, not machine learning with python. There is no tutorial with python.
創建者 Erik C•
This was a good course to see how the basic ML models can be used with clear examples in Python. It was a very good sequel to the Stanford as this course didn't go into detail on the algorithms or any depth in to the math behind the scenes. In fact, you could ignore the equations and still do fine. Unfortunately, I didn't feel I learned enough, specifically about how to tune the parameters and improve the results of different algorithms. The final could be accomplished by simply cutting and pasting the work done in the non-graded 'labs' and providing any level of accuracy scores. I would have welcomed more depth on optimization. Also the hardest part of the course was using matplotlib but you didn't even need to understand it to pass. Overall, I'm glad I took this course. It was very helpful in my learning journey.
創建者 Shane W•
Actual content is good, but i deducted two stars. One star because the pacing of the course is just too fast. The course could really be split into two courses: one on regression and one on classification/clustering. I deducted the second star because the assignments really need to be clearer, especially the final assignment. It would greatly help the people doing the assignment *and the people grading it* if there were more explicit prompts for where you wanted to see, e.g. jaccard score for the knn model, or if you said, "build a visualization that demonstrates the accuracy of knn models for all k, 0<k<20". Being more explicit about the expectations would make the assignment a better evaluation of the student's understanding.
創建者 Some G•
The Machine Learning with Python course was very challenging. The final assignment, though, seemed to require knowledge not yet learned, which made it rough to complete. Also, although I completed the notebook, all of my cells were not visible to the reviewer even though my settings were such that all cells should have been visible to him/her. I restarted the kernels and re-ran my code a couple times and it was finally visible when I opened the shareable link. That delayed my receipt of an accurate score for a few days. Ugh.
創建者 Kerryn G•
This course was well paced, however, it did not go into sufficient detail when it came to explaining the fundamentals of machine learning. The final assessment does not appropriately justify the knowledge one was meant to have learnt during the course. More time should be spent understanding how the models work and how best to tune their hyperparameters to achieve the best state.
創建者 Sylvio R•
O curso em si é bom, mas como a maioria dos cursos online não temos espaço para dúvidas (e não, o fórum não é suficiente).
A tarefa final é muito mal explicada.
Também senti falta de mais Python durante as aulas, que só cobrem o aspecto teórico. Embora muito bom, ao se deparar com o código, surgem muitas dúvidas.
創建者 Parth R J•
very bad course
no proper instructions or explanations in videos
創建者 Farrukh N A•
I have just completed the course and mentioned below are my key pros and cons for this course:
1) I loved the theory and different techniques explained in the course.
2) The presentations were very well made and it helped me to gain knowledge as far as ML is concerned.
1) This is a pretty outdated course, where there are ALOT of typos and coding errors throughout the labs as the coder has left IBM and is working in some other company for more than a year now. Thats is why no one is there to update the course.
2) The title of the course should be "Machine Learning with Mathematics" rather than "MAchine Learning with Python" because the emphasis of this course is on using mathematics to solve ML related problems and that is why most of the libraries and techniques used in the python files were not defined.
3) This IBM's specialization is of BEGINNER level and the inclusion of an INTERMEDIATE level course which requires you have to have some experience in Data Science and advanced level knowledge of Python is just mind boggling to me. It would have been great if a basic level course of ML would have been developed which emphasized on explaining while using Python libraries would have been much more appropriate for us.
4) Lastly, it has confused me while going through this course that numerous times the lecturer spent major time of the lecture in explaining the advanced mathematics which Pythons libraries can easily do for you, even if he told us that remembering of the mathematics is not need. STILL he explained it. I don't know why he did it again and again.
創建者 Fangfang K•
I learned a lot from this course. However, had I known what I had to go through to learn the knowledge, I would not have taken the course; the process is too painful. Therefore I would not recommend the course to future learners. Read my review and save yourself $39.
1) Too many typos, bugs, inconsistencies throughout the videos and labs. The same mistakes have been brought up by students over and over again on the discussion forum, but have never been fixed.
2) Teaching staff do not pay attention to students asking for help. Sometimes when they do answer the question, they give a very vague or irrelevant answer; and when being pointed out by students that their answer is not helpful, the teaching staff do not bother to reply and address the issue. I feel like the teaching staff never went through the entire course themselves so they do not understand our students' concern and frustration.
3) A lot of Python codes are never explained or commented. This is a beginner level class but they expect you to be able to code proficiently; otherwise you are going to be stuck with one line of unexplained code for a long time...
4) The whole course is like a giant advertisement for IBM Cloud, which is not user-friendly at all.
創建者 Anton M•
A bit dissapointed by this course. The main topics were given clear and simple, but there were too few details, saying that all the details are out of scope of the course. But I would prefer to have more information and also more mathematical details (I find the argument that it needs appropriate background strange: if one wants to learn Machine Learning, should already have some basic mathematical background as knowledge of derivatives, integrals, etc).
Another big disappointment was absence of the graded programming assignments, except the final project. Every part of the course had just graded Quiz, but real hand-on scripting in python was given just as non-graded example, and then final assignment basically consisted from the same code.I find this approach quite useless. Also the final assignment had to be done at the IBM Watson website - I guess just for advertisement of IBM services - but this is useless to waste time on registering there, and figuring out how to do things there, if instead could be done inside coursera itself.
And finally, there few some mistakes and typos e.g. in the final assignment, which made everything a bit confusing.
創建者 Joe R•
This course was taught nowhere near as well as the other courses in this certificate track. The code syntax was not explained well at all and it took forever to decipher. The lectures were also not very informative. I would have appreciated a much more in-depth look at the concepts or at least explaining them in further detail. These courses are supposedly for "beginners" but there is no way a "beginner" would be able to get through a course like this without explaining everything better.
The final assignment was also VERY confusing. I would recommend the instructors revisit and revise the course material to make it more engaging and do a better job of explaining the concepts.
創建者 Christine S•
Course subject and materials are good, relevant and deep enough. However, this course, as some others in the IBM Data Science track, holds your hand through so much then just drops you on final projects. The final project for this course did not have full enough instructions; the final bit had not been covered at all in earlier weeks and students are left with a generic instruction of 'you should be able to do x'...without any further guidance.
The grammar and English used in the course materials is poor. This makes some learning and assignments unnecessarily difficult, and it's not fair on quizzes/finals to have a question that doesn't make sense in English.
創建者 Vahid S•
This course material was good but I think it has some issues:
1- The coding levels in labs are so high and not suitable for beginners.
2- the final exam was simple but it had two issues. The instructor pre-split dataset to train and test parts is confusing without a good explanation and the worst part was the peer-graded section. just provide a reference notebook with confusing rubric grading and had a mistake.
創建者 Oliver S•
I liked the videos, but there are a lot of mistakes in the notebooks, especially in the solution for the final assignment (which results in unfair gradings). Most of them were mentioned in the forums months ago, but as with all IBM courses, that I have finished so far, no employee seems to care. None of the mistakes gets corrected, and most of the time, you don't even get a reply from one of the moderators.
創建者 Vyacheslav I•
It could have been very good. But again, one more useless course by IBM. Your task is to copy-paste without asking any question why and how. Graded assignment is a joke. Sample result notebook is useless as nothing is explained, proposed models are bad and NOT CORRECT in a first place. Just give your money to IBM and don't ask questions
創建者 Paul A•
Although this broadly covers major ML algorithms and usage, it doesn't go into enough depth for the content to be functional in any real way. If you've got outside ML experience this is an easy way to learn how to adapt to using Python for ML, but without that you're not going to get even a surface level understanding of how ML works.
創建者 Hakki K•
I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".
Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)
Course 1: approximately 9 hours to complete
Course 2: approximately 16 hours to complete
Course 3: approximately 9 hours to complete
Course 4: approximately 22 hours to complete
Course 5: approximately 14 hours to complete
Course 6: approximately 16 hours to complete
Course 7: approximately 16 hours to complete
Course 8: approximately 20 hours to complete
Course 9: approximately 47 hours to complete
This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.
創建者 George D•
Peer reviews are very inconsistent. Submitted a project 4 times following some minor change from one to the other... only to be 2 points from passing. They want you to have an IBM cloud account and push watson services for this only to have the code crash while compiling. No way to reach instructors.
What a waste of time.
創建者 ubaid m w•
In lab there are many funtion , libiraries Which have been used first time with out any description , then I have to search for each and every funtion or lib which is way time consuming which make this course worst courses in my list.
創建者 Nishan P•
Instructor are going to fast. They are literally reading the slides without proper implementation of the ideas and algorithm explained. Even I can do that, absolute waste of money
創建者 Karol S•
wrong grading on quizes (multiple choice questions which are graded 0 or 1), not clear instructions, who write this course? One of the worst courses i took in years