All over about the course is good but little bit math behind the algorithm was not explained (iterative) and implementation with python also not discussed. Over all you will get to learn many things from this course.

Thanks

All d best

I was not satisfied with the way my final assignment was graded wrongly without any feedback. But overall the course is definitely helpful in introducing machine learning concepts with implementation using popular python libraries

The course is great and has much basic information about machine learning. But, I think it would be better if labs were graded. I personally, didn't put a sufficient effort in them, although I think they are the most important.

The content in the course is very detail and clear. They illustrate each time difference technique where we can use in machine learning. Though, I found the exercise in are a little bit easy, but it's help a lot with learning.

Best course to understand basic working of all algorithms. Assesments are goof for fresher and looks easy if one knows already. Ensemble techniques should also be included in this such as RandomForest and Boosting Algorithms.

Good content, but a few technical issues with Lab.

Possible improvement can be having lectures also about coding ML models in Jupyter, rather than just having theory lessons.

The provided notebooks are well written and clear

Pratical Assignment have helped. Though i found the theoretical part for logistic regression and neural networking to be weak. I Would suggest you if you want to have a in-depth knowledge, do the andrew course in ML first.

Great Course to get started with the practical Machine Learning, This course is for beginners who wants to get to know the Machine Learning Concepts and its implementation.

Great Step for the next courses like deep learning

This is a good course for catching up with fundamentals. Although most of the techniques and algorithms discussed here are not widely used nowadays, they are still good to know and useful for simple and small datasets.

Very informative, I learnt a lot about model training and machine learning techniques. However I found some parts of the materials were jumping too fast to result i.e. not enough step to step explanation for the codes.

One of the better courses in the series. Lab sections can be better with more practice questions. Final project could have been more comprehensive as well instead of focusing on just one section in the entire course.

A good course to start your AI journey with python and scikit learn. Four stars because code should be explained in a video, but it has an advantage that when you search something you always discover something new.

This course provides excellent practical implemented datasets which gets you started but a person willing to do this course must have to learn various things on his own as well to completely understand this course.

One of the best things helped me to understand AI is IBM Lab , It is easy for use it and understandable . This course was so difficult and sometimes I needed to watch videos many times to understand the content .

Videos contain great content, are very clear and to the point. However, the malfunctioning Lab environments really took the speed (and fun) out of the course. Overall it was an interesting and valuable course.

if only financial aid was available for this course it would have been awesome, the content is pretty good, but the labs are pretty confusing as I haven't been able to figure how to register them as completed.

Es un curso muy completo que cubre muy bien los fundamentos básicos sobre machine Learning. Al final de este curso tendrás una noción de que algoritmos son útiles para cada una de las necesidades mas comunes.

Las explicaciones en los videos son bastante buenas, aunque las actividades no permiten comprender del todo lo que se debe realizar para el examen final, cuesta mucho trabajo desarrollar la última entrega.

Good Course on Machine Learning. Organization of Module content is good. The final project is simple and short but peer grading is used instead of having it automated or reviewed by a course moderator.

The course was very concise and very helpful for people who want to learn ML for a career. It would have been even better if there were some OPTIONAL readings so that we can also learn the theory part.

This course is suitable for beginners. One can get hand-on experience on creating machine learning model and basic working knowledge of some classical machine learning algorithms. Overall, good course.

A very good and informative one comes with online lab service. But the price for earning a certificate in this course is bit high that's why i'm giving it a 4 star. But the overall experience is 4.5/5.

Great course! One idea for improvement > Some of the comments in the Clustering and Recommender systems labs are hard to understand. Maybe you can rephrase / add more text to make it more intuitive.

could be split in two courses to be given enough focus. it was very condensed and needed more time and explanation in each section. The instructor was very good but more details would have been nice

Excellent course for beginners to data science field. Would have been better if the final project also included flavor of other ML methods such as Regression, Clustering or Recommender Systems.