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.

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.

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.

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.

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

The course is amazing but the labs are on an external website that is so bad (Sometimes it does not load, ).

I think the overall course content is a good introduction to machine learning and I learnt a lot from it. The lecture videos do not cover any coding while everything about Python is from the labs. So, the most important part in each module is the lab practice but unfortunately the labs are not mandatory and ungraded. I strongly encourage everyone to understand the codes from the labs and complete all practice part of them. That's the only way you can learn the most from the course.

The course is outdated in some aspects. I wish it used more real world problems, and there was a deeper explanation of how the data is treated. The notebooks are used in a third party, which makes the learning experience worse, since the third party system is not automatically graded. For an introduction is ok, but it definitely needs an update on the whole course.

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.

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.

somewhat superficial. I think the course name should be only machine learning, not machine learning with python. There is no tutorial with python.

Nice Course, but I think it should go a little deeper in the math fundamentals of machine learning and explore more algotithms

If optional labs are explained more like data preprocessing and normalization then it would be 5 stars.

Not bad. You definitely need pre requisites beyond python. An understanding of numpy, pandas and matplotlib. Also although these are used in a lot of the code in this course they are never explained. In fact, many parameters are not explained and the authors of this course did their best job to explain things at a very high level of complication. Not much in this course is broken down in simple terms which would have helped a great deal in terms of moving on to other subjects quicker. You are left to decipher everything.I did the course in 5 weeks I scored a 90% on the final and I did the final optional lab and scored 53/53 100%. This course if authored better could be a 3 week course easily. The material is not that complicated once you break it down in a more relatable way which is once again most of the effort in this course. There are also many formulas presented in this course which you dont really need to know. A simple foot note of them would be enough but they are a large focal point for no good reason. The output that you end up creating in the labs is just different averages of the models you create. They dont show you how to output anything cool like actual predictions rather how well the model is performing. If your a tiny bit clever you can sort this out though but its not the focus which makes it way less fun. So to recap (My opinion):This course is not very fun. The material conceptually is really fun but you might never know from this course. The application of the material in this course could be very fun but in this course its not. The explanations in this course could be fun and inspiring but they are instead rote and boring just like the labs. The positive aspect of this course is its kind of accredited you have something to show from your work, a certificate and there is a structure that you have to follow which helps. They claimed there would be projects that would help on a resume... not really at least nothing someone would be impressed by that didn't look super generic.

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.

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.

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.

This course has all the fundamentals and in depth learning videos. For me it had many spelling errors in the material that were either annoying or misleading which for a company like IBM not good enough. The guidance around the final capstone project could have been clearer for me, and I had several issues logging into IBM's Watson Studio. I did like the peer graded assessment strategy.

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.

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.

It is good overall but missing lots of useful ML algorithms such as boosting algorithms, density and hyrachial clustering algoritms. It also didn't mention about overfitting and underfitting cases comparing with training and test scores.

very bad course

no proper instructions or explanations in videos

I have just completed the course and mentioned below are my key pros and cons for this course:

Pros:

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.

Cons:

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.

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.

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.