Feb 07, 2019
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.
May 26, 2020
Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!
автор: Jonathan L•
Dec 06, 2018
I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis
автор: Imran R•
Feb 03, 2019
Thank You Mr. Saeed Aghabozorgi for designing and delivering such a immersive course, I found lot of pointers and specific details associated with many interesting topics in Machine Learning.
автор: Holly R•
May 08, 2020
The descriptions of the algorithms in the videos were useful for getting basic understanding. There was almost no discussion of the math behind the algorithms and no explanation of how to use the python ML tools. The exercises were primarily executing someone else's code and did not require much effort. Although I now understand the basics of some ML algorithms, I would not be confident in applying them to real problems based on this course.
автор: Derek A•
Jun 03, 2019
Was a 3 stars until final week. Stuff is explained and is written poorly. I honestly felt like I got shammed by last week. I had to look online at other YouTube videos and forums and I am just not happy with what I got out of this course. I will be doing Andrew NG's course on YouTube now..
автор: Suresh S•
Apr 17, 2020
I liked it very much and was able to clearly understand the usage in programming language with ML related libraries. Thanks to IBM friends and Coursera for providing the expertise and the platform.
автор: Juan V P•
May 08, 2019
Very interesting subject, and very well explained. Even if I miss more concrete code examples, I can always look for it, the theory and the logic behind it was explained flawlessly.
автор: Sumedh K•
Oct 18, 2019
The course is amazing. It provides with Mathematical equations for all the algorithms taught and coding is done with real world cases as well.
автор: Bjørn I A•
Jun 25, 2019
I liked this course. Nice to see how math learnt in theory years ago can be used in practice in some of the models.
автор: asher b•
Dec 06, 2018
puts a lot of the previous courses all together. challenging, but doable.
автор: Vivek R•
Mar 20, 2020
WORLD BEST STUDY'S MATERIALS ARE AVAILABLE ON COURSERA.
автор: Chetan M•
Mar 21, 2020
The course was well described. Thanks Man !!
автор: Chang C•
Dec 08, 2019
I am very frustrated with the course's final project. Please, when you ask for tuning meta-parameters, either be specific or do not provide a false out-dated solution where there is no tuning at all in decision tree, svm, nor regularized logistic regression. Not every new-to-stats understands your misleading instruction of the final project or can be capable of grading according to what is actually correct.
The instructor should be more aware of this issue. I ask for a refund, it doesn't worth my money!
автор: Jim F•
May 03, 2020
Using IBM Watson Studio 'Lite' plan is a huge pain in the ___. I had to use 4 different emails to start from scratch to submit the notebooks for peer review. The course's instructions don't mimic the actual site - sometimes I wonder if they're referencing the same site in the instructions. You can learn this information elsewhere without added the headache.
Apr 30, 2020
Its interesting given the title the lectures never mention Python or show code ; ) That's left to the ungraded exercises. I liked it this way. Getting the good background on the algorithms independent of language or library, and then applying that in the labs is effective. I will refer back to this class as I continue learning about ML.
I had trouble getting my final project graded - but realized I hadn't shared my project correctly (at first didn't share code cells), and had to save a `version` of the notebook so my edits would be available to the other students to be graded. Leave yourself extra time for your peers to review your project, and check that the shared link to your notebook shows what you expect. You don't need to post in the forum to get your project graded - lots of students were doing that..
автор: Stephen P•
Mar 10, 2019
Lots to learn in this class! Week 3 was definitely heavy and challenging in the middle of it, but the course really builds up well and makes sense by the end of it and I understand why those topics were combined as they were. I found the labs most helpful when they included # hashtag explanations/documentations when introducing new code to explain the different parameters and reasons for using them, or if establishing parameters in the code with explanatory definitions/names to guide the user through new operations. In the very last lab, I think they included a link to the pandas API reference page with that specific new operation. I found that really helpful because I had already been going to the pandas page to learn more about other new operations as they were introduced in previous labs.
автор: Caterina F•
Feb 27, 2020
Machine Learning with Python is highly informative and very well presented. It wasn't easy, it requires a good understanding of math. Complex concepts of machine learning algorithms are explained clearly.
After the course, you will have a solid awareness of how machine learning is applied to the real world and how to use the skills like, sci-kit learn and SciPy from the Python language.
Excellent support of the labs and the Notebooks provided. The final project will be a challenge for what we have learned.
I strongly recommend this course.
автор: Jeremiah J•
Feb 11, 2020
This was MILES ahead of the last IBM course I took (Building AI Application with Watson APIs). The part that I thought isn't great is the use of other students to "grade" the final project. I definitely understand that you wasn't have hundreds taking the courses at any one time, so that might be the best way to get through the projects. I hope that there is some sort of feedback loop so that if a project was failed by a classmate more than twice, the next submission goes to a REAL staff member for review. Thanks for the great course.
автор: vatsal n k•
May 03, 2020
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.
автор: Nandivada P E•
Jun 12, 2020
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
автор: Rajdeep S•
Jan 15, 2019
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.
автор: Pamela W•
Apr 10, 2020
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.
автор: Erik C•
Jul 04, 2019
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.
автор: Shane M W•
Jan 07, 2020
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.
автор: David D T•
Jul 08, 2019
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.
автор: Nathan E•
Apr 16, 2020
The course covered quite a wide range of topics in Machine Learning, which was great. However, the sample code was not commented as much as I would have liked, at least for visualizations of the results of the machine learning algorithms, so I don't feel very confident that I would be able to replicate many of those on my own. The in-lesson exercises mostly consisted of following examples arranged by the instructor, there weren't many opportunities to challenge yourself with exercises and get feedback.