I really enjoyed this hands-on course with a lot of practice. The difficult part was the week 1 when we had to set up the virtual environment, and pass the first quiz. I believe that if there is more support at the beginning, more people would have stayed on to finish the course.

Thank you!

Assignment instructions are not very clear and often not formatted properly - multiple questions are "glued" in a single block. It would be good to spend more time on GraphLab API at the beginig of the course. iNotebook did not work on coursera.org, I had to install a local version of Python.

The course has a really interesting approach, and I have enjoyed it, but as Turicreate (previously Graphlab) has not become a mainstream tool (on top of the difficulty to use it with Windows), I would rather have also some exposure to other tools, as Scikit-learn and Tensorflow, for example.

Case study approach is really helpful but we need to understand the formula behind those deep learning scores which i think has not shown in this course. Second, if you can provide more videos for coding then it will really helpful to do the "Programming Assignments" which i think is tough.

Well designed and executed in the main. As videos are recorded once but viewed thousands of times it would have been nice if they had taken more time to write a script and look more professional - also mistakes in the videos that don't match the current material are few, but very annoying.

Good Course for someone wanting an overview of techniques. You would not be building something very cool after this. Course assignments are not very challenging, some good questions must be included.

Some mathematical part should be there. Expecting it in other courses in specialization.

I found this to be an excellent course. Both the instructors are excited about the work they are doing and do a good job of teaching the materials. I don't have a statistics background and college calculus is more than a little fuzzy. I was still able to understand most of the course.

Pros:

1. Practical and hands on approach

2. How multiple problems like prediction, sentiment analysis, text retrieval etc can be mapped to a common ML model

Cons:

1. Uses DATO toolkit which is a licensed tool.

2. Maths behind any used technique is not discussed, which I think is an issue.

Very nice for those of people who want to learn the concepts of machine learning. However, libraries that used (which are not pandas and scikit-learn) was not satisfying for me. Googling about those libraries was totally difficult while there are lots of documents using pandas and scipy.

A great high level overview and introduction to machine learning. The topics broadly cover different machine learning algorithms and their approaches. The only downside is that the proprietary library they use to teach the concepts is incredibly expensive for an individual to license.

It easy to start and content is less mathemetical and ready to use knowledge. And tool are helpful to start playing with. May be later go into details . It's perfect for software engineer not having knowledge of ML but wants to start. One can play with data after this course I think.

This was a great specialisation taught by great professors, but I'm very frustrated that the final segment on deep learning was never finished! Coursera needs to fill this gap since it is hugely important topic and isn't covered adequately in any other Coursera material right now.

The course is designed very well, nice way of teaching taking practical examples , it is just that working with pandas wasn't easy,it took a lot of time even to find syntax and graphlab wasn't working tried searching on various forums. Please add ski-learn and pandas syntax too.

Pretty good bird view course for entering the world of machine learning. I liked how the course was designed and its contents. It provides us the machine learning ways of thinking and doing, and get us ready to move further and deeper in the path of machine learning. Recommended.

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