Great course to review concepts and learn some new. Using graphlab was great to go through the concepts quickly however it is not the best approach for real life applications. I'm hoping the next courses will go deeper on how to actually work through the models and algorithms.

Very good course. I look forward to starting the next ones, in the context of the ML Specialization.

One small caveat: I would have preferred using a more widespread and open source ML library, than GraphLab Create, which I am not certain to encounter again in the "real" World.

A great intro course to machine learning concepts, the only problem with me is the environment, its course dependent and don't feel like it can be widely applied in various fields

i would love if tensorflow or sikitlearn was adopted through the course

Great course though !

Great course! I think much of it will make a lot more sense in hindsight after I've gone through some of the other courses in the specialization. The main reason I'm not giving it a full five stars are the many mistakes and ambiguity, especially in some of the assignments.

A parte teórica do curso é muito bacana e aplicada, porém a biblioteca utilizada ao longo do curso não é a mais indicada, dado que muito do que foi passado caiu em desuso e também já existem bibliotecas mais populares para desenvolvimento de sistemas de machine learning.

I really liked this course because I was able to actually implement different aspects of Machine Learning which helped me understand the underlying concepts better. I wish that the algorithms were taught with a little more specifics, but overall it was a nice experience.

Top down, concept to underlying details learning approach is welcome, as is the informal communications style and traceability of instruction to quizzes. Only negative is some confusion regarding what was initially versus currently planned for the entire specialization.

Very nice designed course!

Only flaw is its stuck with a paid license of Graphlab and doesnt focus on free source so that algorithm can be used with the help of free modules in future.

It would have been better if the examples were done using free tools, like scikit learn

Machine Learning Foundations was an excellent overview of the many areas within machine learning and how these techniques can be applied to solving real world problems. Now, I look forward to continuing with the remaining courses of the Machine Learning Specialization.

The course was great but the material is not up to date and the support documents are not aligned with the video. The last course about the deep learning was quite messy and the programming assignment was not clear with many many repetitions. That one was not good.

This course is very useful to have a practical overview of the machine learning algorithms and techniques with out diving in complex topics (at least at the beginning).

It's also useful to learn a high level library to manage machine learning algorithms and concepts

The concepts on recommendation systems could have been explain better. Having to use TuriCreate instead of open source libraries is a major drawback. Turicreate is also so high level its hard to understand whats happening under the hood. Everything else is great.

Most of the course was fantastically composed but 15% of it felt a little bit sloppy. I was lucky to be not the pioneer of the course because of the discussions which helped me out quickly in these rare occasions. I would give 4.5 starts if it would be possible.

Great specialization series! The way of covering concepts is very interesting. I find it somewhat difficult to solve the assignment using Pandas and scikit-learn. The assignments should be provided with enough help for solving them using these open source tools.

Really good theoretical introduction but couldn't do some of the tasks because my python skills weren't up to scratch, however the course says you don't need any previous python knowledge, so I disagree with that. However, overall a really good introduction.

Generally speaking, this introduction-level course is great, which gives machine learning beginners some intuition in this field. But it is not enough for finding a job or doing research, I think finishers of this course still need to learn more to advance.

Very good practical approach for an introductory course, however you should have some basic knowledge of programming otherwise it'll take you too much effort and time to complete the tasks assigned. It is also great that Python is being used in the tasks.

The videos are a bit old still the course is very helpful and really enjoyable. The Case Study approach is something that really help me learn things and keep my interest engaged. Thank You prof. Carlos and prof. Emily for making this course a great one.

It was a good introduction. Although, I would say that, being part of a specialization, I would prefer a more brief introduction, because very important points are left for the future and you have to spend too much time in a content tha is incomplete.

The last module was a bit hard for me to understand, but everything else was presented in a very clear way. Great class overall and highly recommended for those with some basic Python skills and a desire to see what machine learning is capable of.

this is an excellent course , for those who really wants to learn machine learning . It has a good lectures and courses and programing assignement . but there one small program is that it uses graghlab create theat works only with 64 bit computer

I would give 4.5 stars if I could. This is a very good introduction to the whole Machine Learning Specialization series given the topics covered, especially with practical cases. However, as a standalone course it lacks depth for a 5 star rating.

Very good material with a lot of real-life example for having a high level intuition on basic machine learning algorithms. However, the assignments are not very challenging. I hope this will change during the next courses of the specialization.

Really great intro to machine learning - I think this class would have been better if the programming assignments had been a bit more difficult, but overall really enjoyed this class and looking forward to continuing with the specialization.

The course was interesting and the instructors seemed to care about the outcome. The issue on this course and this specialization in general is the use of their own private software instead of open source frameworks like pandas and sklearn.