At times, this course feels like an extended advertisement for Graphlab Create, which was founded by one of the instructors. The case studies are all quite interesting, however. I would not recommend this course, except that the following course is awesome and this course is helpful preparation for the regression course. The best part of this course is the interaction between the instructors, who are very fun.

Emily and Carlos are so cute and they gave pretty clear lectures, I love them so much!!! But the programming assignments given are so simple and easy, and the theory behind the codes are not explained explicitly. Maybe because it is the first course of the specialization, I will take the following courses to see whether they are much harder in the later ones and more useful to improve my machine learning skills.

Pros: Really fun to watch. Great introduction to several ML algorithms. Offers specific examples that you can follow along with (Python).

Cons: Uses a proprietary library to demonstrate concepts.

Overall: At first I was concerned if this specialization would be too difficult, but this specific course was surprisingly easy to follow along.

Note, I highly recommend the Data Science specialization here on Coursera.

It seems a very good foundations course, it would be great to dive into more technical aspects, specially because I suppose the majority of us has a background related to maths, statistics, cs, etc. Very useful indeed, hope the studd that comes ahead gets even better. Congratulations. Also, it would be great to learn these methods using other ML libraries, like open source / free ones. All the best, Alexis

Great introduction to Machine Learning Specialization and the various algorithms employed in the different courses. In my opinion every Specialization should have an introductory course that covers the various different things that will form the focus of each subsequent course so that students do not start the Specialization and find out down the line that the Specialization is not quite what they expected.

Me agradó haber manejado con Python herramientas de aprendizaje de máquinas. El curso pretende ser una breve introducción de las principales técnicas y pienso que lo cumple. Sólo me hizo falta que trataran el tema de la creación de las bases de datos, porque siempre se hicieron ejercicios con datos aportados por los profesores, pero no sugirieron la creación de datos propios en el formato de la herramienta.

Very good general overview of ML. I put 4 stars instead of 5 because too much focus is made on Graphlab for the hands on exercises. The focus of this course is not on the tool itself however one should know that this is the primary library used for the exercises. Details are given for those who wants to use other library but the foundation of the course are clearly to present and showcase Dato's product.

The content and articulation was excellent. Inspired and motivated me to continue the course in spite of my struggle to understand certain concepts. There are lot more statisticians out there who are heaps better than myself and it was excellent experience to read the questions and answers in the discussion forum. Will continue the course and get my certification and enroll for capstone project as well.

I really enjoyed the practical part of showing concrete examples and direct applications of Machine Learning algorithms.

Instructors are dynamic and have made efforts to show enthusiasm.

Quiz are too easy though. Completing most of the work would only require an hour or so per week. I hope that the other courses are way more intense and require more work.

Overall, I would recommend this introductory course.

This course is an excellent introduction to many artificial intelligence concepts, although you do not go into any of the topics in depth. My only complaint is that the material has not been updated to reflect the cancellation of the last two courses of the specialty, so the instructors spend time talking about nonexistent material. It would have been preferable to use free software from the beginning.

This course is pretty good for someone who wants to learn how to implement machine learning models, how to train data and how to make the most out of machine learning. The only issue i found is they are using GraphLab a lot more where as SciKit learn is the industry standard. Overall, its a good learning way, models and other things you can learn on your own once your basics are clear. Great Course.

The theory provided by the course is really good and I will highly recommend it as an introduction to ML. The practical applications are not as good. They feel rushed and not particularly well explained. You usually get a recipe that you follow through using a tool that hides most of the details behind the theory. You just feel like you would not be able to do the assignments without the tool.

Loved the enthusiasm the instructor show while teaching! However I would suggest either to update the course video to consider Turicreate or change the assignment back to Graphlab, as it can be troublesome. Also installing graphlab on windows was a big challenge (at least for me), would really appreciate if there was one video explaining how to do that!! Overall content wise it was nice.

Excellent course! My only "complaint" really is that I wish the instructors would use other tools besides GraphLab. I realize that this does make it a lot easier, but I'm sure many of us work in environments where we cannot use GraphLab (for a variety of reasons) and it would be most helpful it they demonstrated how to accomplish the same thing solely using open source alternatives.

Love the professors and the curriculum. The video design is quite annoying though -- if I want to rewind to a particular place, oftentimes I would accidentally press the configuration buttons because they're so close to the timeline(?) (I'm not sure what it's called). Also, it would be great if the subtitles didn't have a background color, as it blocks out a lot of the video.

Great introduction to the various topics of the specialization. Introducing each of these topics in a generic way helps to get a sense of what they are about, and what to expect from the specialization. It might be a good idea to be explicit about the fact that this course really only has value added in conjunction with the other courses, and not really as a stand-alone module.

It was really good.

Pros:

Has really nice assignments.

Teaching is really good.

Cons:

Should've used and open source package. Graphlab is good, I accept, but I wasted like 4-5 hours trying to install it, because some or other errors or dependencies,. I mean some consideration should've been done about an easy smooth method for it, for a beginner like me, it was really frustrating.

The installation process of jupyter and ipython notebook can be more clearly explained because many people including me had a tough time figuring out the right way to install and get started with the assignments. Many people are stuck there only for 2-3 days and give up the course.

Otherwise I enjoyed the course with the different approach of teaching(the case study approach)

Very good course and inspiring case studies! A small fix needed is to change the order of the concluding remarks of each week and the Jupyter Notebook exercises. In the film you always refer back to the Jupyter Notebook exercises, but when I look at the film (following each week very linearly) I have not yet done the exercises. Thank you for a fun and interesting course!

I really enjoyed the intro, I would suggest that you take a bit of Python, and Brush up on your Calculus, because you will be needing it. It's possible to keep up conceptually, but I barley made it, and am taking some Python, and Calculus to make sure I'm not waisting time learning skills that are needed in the course, and can focus on the material being presented.

This is an awesome course for those who are curious of what machine learning is all about and want to get a broad overview. You don't really have to have programming background, just some basic math will suffice. However, if you look at some background and practical implementations, Andrew Ng's course is the one to go. This one is too shallow to anything practical.

The materials are very interesting. Great introductory course. There are a few problems that need workaround if you are using Amazon AWS to complete the quiz. Need some FAQ section for people who are using Amazon AWS. Wish there are some documents reference python/learn. Excited to take the deeper course but need to upgrade the computer instead of using AWS again.

Very good introductory course, especially to someone with little knowledge of ML. My only frustration is that I have some background in machine learning, so not very much new material- I would have skipped this and jumped straight into the the regression models course, but you need to complete them all to unlock the capstone (which looks really, really cool!)

This course is very practical for starters in Machine Learning. The ML methods it teaches are very fundamental, with clear and intuitive explanations about how and why they work. This course is recommanded to those who want a hands-on learning experience and who are not statistics experts that do not care much about the mathematical proofs of the algorithms.

Great overview of machine learning concepts. The content is structured in a helpful way to give you conceptual understanding of machine learning. Some of the quizzes and materials are a bit messy (most of the challenge is figuring out what parts are out of date or related to course material that previously existed). For the price, it's definitely worth it.