- Could be heavier on the mathematics, which would generate a deeper understanding. - Also if you will not use a specific information, you should not mention it or provide further reading. - Quizes need a rework: answers in some cases are more like "guess what i want to hear", particularly when more than options are definitely correct. - Too often: "Those topics are not in the scope of the course." - The hands on labs are alright all in all another disappointing course...

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.

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.

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

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.

I'm finishing this certificate program because it would be easier than starting another one from scratch. I've been disappointed with most of the courses and this is no exception. There are mistakes, typos, and poor grammar throughout the course. They have a system to report mistakes, but I should be getting paid to fix your course - not paying to fix it, right? The quizzes are an unnecessary waste of time (they ask very minute, arbitrary questions about videos that are just meant to give you a brief overview).

The labs are the most / only useful aspect of the course because that's where you learn actual code - but they don't explain HOW the code WORKS. They just say what the code does and then they show it to you. There's a difference, as any good teacher knows. This course was clearly created by data scientists, not teachers (and certainly not masters of the English language). I would recommend this certificate program if you already know python and data science and you are just trying to earn a badge that will look good on your resume.

The course is very shallow. It never goes in depth with the algorythms, neither in a mathematical sense, nor in how they are implemented and best used. They don't even cover hyper-parameter optimization using cross-validationThis is not ok for a final course in the IDM Data Science Certificate, especially because Regression was already much better covered in the Data Analysis with Python Course. Moreoever, the Final Assignment features an unbalanced dataset, for which the course does not prepare students enough. If one tries to copy the methods used during the course without reasearching much about this on their own, they will train models that would be unacceptable in a real-world scenario. Worse still, the "model" answer provided does exactly that.

I learned a lot, but the final assignment is just a mess - on so many levels. My biggest takeway is that even well-rated courses with qualified instructors end up causing material issues. The quality of the final project implies that people will be trained here to create ML models that will have real world consequences and will not be properly understood or validated. And that's without looking at the huge number of typos, bad programming techniques, and more. Had to give up on the final project due to those difficulties after spending 10 hours of my very early mornings without any reasonable progress.

As am going along in this IBM certification, the quality of courses is getting depleted. This course has by far the worst standard in terms of quality of content and assignments. The worst part is that they encourage you to use IBM cloud services which are the worst and require improvement themselves. But the worst part was the peer guided assignment. With no clear instructions, peers that have no idea checking your assignments and long delay for waiting the grade for it, god help you! Don't waste money on this course. Hopefully, coursera takes actions against IBM if they don't update this course.

I hated that all the instruction was all math and none of it python. The instructor was completely uninspiring. You really should have instructors with some level of charisma. The course was littered with technical errors. This is the worst MOOC I've ever taken. It is a crime that people actually have to pay for this.

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.

Whoever is from the CIS countries beware of IBM's faulty practices.

It contains tons of marketing for IBM products. Without those products you won't finish many assignments. Moreover, IBM blocked my country from the ability to create an account on their services. So, I can not even finish those courses.

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.

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

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

The course was going well with the videos and labs, until the capstone peer-reviewed area. Disastrous instructions, poor supervision and assistance. I am appalled.

Insufficient explanation, bad lab experience, and the final assignment was a nightmare.

Video is short, so you got to figure out things by yourself.

Videos and assignments are very repetitive. It induces students to copy dull code rather than think about solutions

Everything taught int his course ends with a line "unfortunately it is out of the scope of this course"

outdated notebooks, had to google everything anyway

we are not able to get ceritficate

Great course! Get ready to learn, code, debug, sweat, learn some more, fix your code, then finally smile when your ML models work smoothly.

That last statement described my workflow during the final assignment/project of this course.

Quite simply, this course was brilliant because not only did it bring everything we've learned so far together but it also built upon the last course and properly introduced us to Machine Learning and its applications. In his videos, Saeed successfully breaks down complex topics into digestible byte-sized content and ensures that you intuitively understand what is going on.

One of the best pieces of advice I have received in regards to my learning and in life in general is to make sure you have a strong grasp of the fundamentals and these become building blocks to much more complex topics. That in a nutshell is what I believe this course has done for me.

To those who are reading this review, trying to decide whether or not to take this course... just do it! What are you waiting for? No seriously? This might be one of the best decisions you make this year.

If you've been racing through the other courses up to this point, I advise you to slow down once you get here and really try to digest what Saeed has taught here.

Watch the videos, pause, take notes, rewind, continue watching, learn, code. Iterate.

Dear Coursera and IBM,

I am writing to express my deep gratitude for the opportunity to learn new skills and knowledge through your world-class platform. As someone from a rural area in Pakistan, access to quality education can be limited, and I feel truly blessed to have been given the chance to learn on such a reputable platform.

The courses and instructors provided by Coursera and IBM Skills Network have been nothing short of exceptional. I am especially grateful for the professionalism and well-formatted courses, which have allowed me to easily navigate and learn at my own pace.

I believe that the skills and knowledge I have gained through Coursera and IBM Skills Network will be invaluable in my personal and professional life, and I am excited to apply them to make a positive impact in my community.

Once again, I cannot express my appreciation enough for the opportunity to learn through your platform. Thank you for everything that you do.

I love every bit of this course. It is very informative and the explanation by the instructor is second to none. He explained most of the concepts especially using real life scenarios like customer segmentation, detection of cancer and many more. Using these real life examples in the explanation made me understand the course very well and also appreciate machine learning. It will be very easy with anyone with mathematical background though people that are not mathematical inclined may have some difficulties understanding some of the concepts. Nevertheless, going through the lab section will make you understand the concepts very well even if you didn't get all the theoretical concepts. The final project was also centered based on what was taught and easy to follow by anyone that paid apt attention to the lectures and followed duly in the lab exercises. Kudos to the instructor.

The course start with introduction to Machine Learning, with various industrial examples and applications along with libraries used for Machine Learning. Understood how supervised machine learning is different from unsupervised machine learning. Then learnt the concept of Linear, Non-linear, Simple and Multiple regression, and their applications, also how to evaluate your regression model, and calculate its accuracy.  

Practiced with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Introduced with main idea behind recommendation engines, then understood two main types of recommendation engines, namely, content-based and collaborative filtering. The course ends with Peer Graded Assignment to apply all the ML modeling learned.

Thanks to IBM and Coursera for this great learning experience.