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

Explica todo en detalle, pero no explica los codigos en los videos ni como implementarlo, auque tiene sentido porque todo se actualiza a un ritmo increible, estaria bueno que lo consistente se explique en los videos tmb.

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.

The theoretical part is well done and very interesting, but at the end of the course the explanation regarding the use of Watson Studio for the exercise and the final test is quite misleading.

This course walks us through the fundamentals of machine learning methods. The capstone project is very useful for those who have previous knowledge of machine learning and Python programming.

I think PCA would've been a very useful clustering method to teach. AUC are a great way to measure the effectiveness of a logistic regression algorithm, it would've been useful to learn here.

Very nice course with very less time .

But i though there should be some mathematical explanation in detail what i observed there is lack of mathematical explanation.. overall course is good