A very good course to learn the basics of ML. Several in-depth topics are not covered stating that they are out of scope for this course.

The course allows us to use an online tool for lab work and assignments with many relevant libraries, thereby avoiding any software/library installation issues, etc.

There are relatively less number of videos but they are to the point.

Labworks need to be self-learnt(no separate videos for code), although the notebooks that are shared tries explaining the code a bit.

Overall for a new learner in this field, it's a good start.

Marking my score as a 3.5 as I cannot choose fractions:

I think the course is fantastic from the academic point of view, I had taken courses from other sites and this one clarified all doubts I had in regard to the mathematical nature of each of the studied methods.

The missing star (and a half): little to zero explanation on the algorithms. Yes, it poses the challenge of self studying but at the same time I believe some codes might be to advance for a person with average exposure to the language to figure them out by themselves at a reasonable pace.

The elbow method for evaluating the best K in KMeans was mentioned in a video but wasn't demonstrated in the lab. You can find information on it online so its not a big issue but it would have been nice if it were included. Another method, the silhouette score, could have also been mentioned. Overall the course was very comprehensive but if you want to get the most out of it you need to make sure you understand all of the code in the labs which can take some time and research. Some more documentation of the code can really go a long way.

The course nicely introduces the learners to Machine Learning, it's commonly used algorithms and it's applications in various fields (which is the best part!). It will surely help budding Data Scientists in getting insights about Machine Learning and it's working principles. Instructors are awesome and so are the videos.

Though labs can be made better for people with no/little programming background, I would still suggest this course to learners interested in the field of Data Science. A good one for sure. And definitely interesting!

Pros

1. Course covered the material the well.

2. The material is explained in easy terms as needed for a 101 course.

Cons

1. The final submission pushes users to use an IBM product. The steps to get access is out dated. Also the allocated IBM resource is limited and asks for payment once the time limit runs out. This is not in spirit of education.

2. Final submission is poorly organised.

3. Final submission is peer graded which means others who are taking the 101 course are grading. This is not a good grading process.

Mostly a very nice course introducing the basic ideas behind many standard techniques together with the basics on how to implement them. Gives a good start to learn ML further. One star lost from the fact that some of the quizzes are badly designed -- multiple choice questions with slightly ambiguous answer possibilities where you get no partial credit nor any feedback on what went wrong. I still have no idea why some answers were right or wrong as I just had to try too many different quesses to get a passing grade.

Great video material and clear structure. I also like the JupyterLab integration. The exercise notebooks need some cleaning up though: Lot's of grammatical errors, inconsistent coding conventions (snake_case vs camelCase), poor variable naming, programming mistakes resulting in incorrect accuracy scores, outdated libraries (there are provided functions for rendering confusion matrix and plotting decision trees in sklearn, which could be used). It shows that the notebooks have not been created by Python experts.

I really enjoy this course. It teaches me a lot of basic machine learning model, method and data analyzing technique. However, I still recommend that it should have coding assignment for every week exercise. It is because learning from video is simple but hard to do implementation. The best way to learn data analysis is to implement or do the real stuff by ourselves. It is necessary to put an assignment to force every learner try and error. This is my opinion for this course.

The rubric for the last assignment was too arbitrary. People with little to no machine learning experience will assume that submissions have to be cookie-cutter copies of previous labs in order to achieve 100%. I would put force students to put random seed on models in order to achieve similar results to achieve more homogeneity and therefore an easier way to grade. Perhaps you could put a section at the end that allows for further parameter tuning if the student so desires.

The course is very basic and does not go into too many details. I would suggest it for beginners who want to understand the main concepts, but I am not sure I would put this in "intermediate" level, I honestly hoped to do more advanced stuffs. The labs are very easy and fast, it's basically always the same commands to run and you program very little. Lessons are very clear and well organized. The reading material, however, is full of mistakes and not clear.

When I started the course, I was expecting more of python libraries and exercises with Machine Learning in python. However the lab parts was ungraded practices so there wasn't an instructor for me to tell and teach how I can imply my vision and knowledge of machine learning on python. Other than that, It's a great experience and the lessons are really clear, simple and teaching. I'm always enjoying the videos while I'm practicing and learning.

Good grounding into machine learning techniques with python. Bit slow at times and would like to have more emphasis on the application of techniques on real data sets e.g. dataset requirements and effectiveness of algorithms on datasets of varying size, and how to avoid overfitting etc. Also it appears as though the requirement to sign up to IBM Watson Studio is not actually required although you are heavily led that way.

The course is well done and covers many of the basic ML concepts. The reason I gave it 4 instead of 5 stars is because the final assignment asks you to do something that wasn't covered in the course, and it's not very clear either what they're asking you to do. I was able to figure it out, but it was a bit frustrating at the time (especially since I got all the way to the end and realized I had to do something different).

This course is great for begginers. Basic theory of simpliest algorithms and techniques is given in really simple way. I enjoyed to listen to videos. However, there is not enough practice coding. Final project was the only challenging task during the course. Another drawback - misprints. In addition, goals of the final project were not clear as for me. To sum up, this course is good just for basic theory review.

The course is good but sometimes the exercise texts are not very clear and some of the lessons are very straightforward, leaving many doubts. The course should have a larger series of exercises and an automatic correction system that facilitates the review of the exercises. In addition, it would be interesting to have a module on how to use IBMDB2 without the online platform, but through Jupyter on the computer.

Learning this course, and specially after Week6, I strongly felt to have some background on statistics. I had to replay vedios multiple time to get the concept. Also the content of the course is very solid, but sometime I felt there is not much explanation of approach, sometime intermediate steps reasoning is missing. Final assignment is very well laid out touching all the models you learn in this course.

The material is comprehensive covering almost all of the popular models. Unfortunately, the peer-graded assignment only covers classification models so the practice on clustering is lacking. For real world problems, this module is probably the most useful so it would be beneficial to include more practice on clustering for examples. Overall, it is an interesting course with lots of new ideas for beginners.

I particularly enjoyed this course. It is easy to understand it even with a basic knowledge of Python. Lab exercises are well-writen and very helpful for the completion of the course. I think it's a great introduction to programming using SciKit Learn. Personally, I would have liked to learn a bit more about the mathematical background of the algorithms but maybe this is out of the scope of the course.

Pretty good course, but you REALLY need to put in your own time to get anything out of it. You really could probably complete this course by just copy-pasting into the assignments. I wish there was slightly less hand-holding throughout the course and more having to do more work on your own with proper guidance, rather than just "here's a video" then "here's a notebook. Run it and see what happens."

This course is the perfect for one who learns the basic of machine learning and They will make sure you learn it percectly but i give it only 4 stars because the lab session was not explained by the instructor although it was liitle bit self explained by the notebook itself it would be better for us if you explain some tougher lines atleast.Overall the course was an excellent one.

I enjoyed this course and felt like I learned a lot! The reason why I'm not giving 5 stars is because some of the assessments need work -- instructions and wording on questions were either confusing or contradictory (for example, on the final project you are asked to find the best k value for 4 different types of ML algorithms even though only one of them has "k value" defined).

The course was good, generally. Instructors as well. I had used IBM Watson and Jupiter Notebooks which was really usefull. But it would be great if you add more real world examples for algorithms use cases. Errors in the presentations and in the Jupyter workbooks, which were mentioned years before, and still have not been fixed are really unprofessional. Anyway, thank you.

This course is relatively good. If you are looking for a introduction to machine learning this is the course for you as it covers most of the methods over a short period of time. The downfall of this is that the algorithms are not covers in detain in particular their optimization and limitations.

Also the exercise are done on the IBM development platform which is garbage.

The material was good but the servers for the ungraded projects could use some work. I had connectivity issues with each project I tried to attempt and even now when I tried to reference the material to improve my models I could not access them. Other than that I thought that this course was very informative and helped me become an overall better programmer.

Personalmente me hubiera gustado poder acceder al certificado de honor del curso, pero no tuve la capacidad de realizar el laboratorio debido a que necesito mejor preparación. Opino que enseñan algo muy básico de programación y mucha teoría. Entonces me siento en la capacidad de responder definiciones técnicas pero no de poner construir un código desde cero