the course seems outdated in many aspects, the support isn't available to clarify doubts and the documentation isn't updated either. Moreover, the software support has ended.

A good course, but the tools are a bit dated and it's showing its age.

I think this course is outdated as they are using python 2 also the platform they use for machine learning only supported by python 2. Due to these limitations, I too was unable to continue this course. Because every time you have to work with new libraries you have to uninstall python 2 and reinstall python 3.

Instructors or TAs are not available in discussion forums. and the course is focussed on promoting "graph lab" proprietary package of the course sponsor. Maybe you can have a look, not beneficial if you are serious about learning ML.

This course has used windows OS and application built for it. But the library/application is no longer supported on windows. So this is really a big problem for windows users.

Overly relies on a paid software (free for the course) called GraphLab. The course can be completed without GraphLab, but expect little / no responses to questions.

excessive use of GraphLab create which is not an industry standard.

Two stars because of GraphLab! Otherwise great concepts!

This course would be okay if it weren't for turicreate, a Python package that's supposed to simplify things. If you have Linux or a Mac, it will do just that, but if you have Windows steer well clear of this course. The lecturers haven't considered the possibility that anyone might not have Linux or a Mac. All the faffing around getting turicreate to work (I did it once and I'm not doing it again) wasn't worth my trouble so I ended up guessing the answers to the quiz questions (you're allowed three attempts every eight hours) just to get this course out of the way. I'll use something actually accessible for the remaining courses, namely R.

The course content is really nice however it is nearly impossible to install the turiconnect to my machine as it wont support Python Version 3.8 and i saw many people complain about the same.

I tried with Anaconda,Ubundu WSL and then i learn i need to be a programming expert to install this software and i do not think it is worth to put my whole effort just to install a software.

Atleast i would expect the instructors to give a detailed description how to install the necessary tools then it will be helpful.Hence i wont recommend this course to anyone

Faced too many issues during oncourse period.

Still feel the teaching is outdated from software point of view

Issues was started the moment the .zip files had to be extracted and to be fed in Jupiter notebook.

My suggestion to coursera: Kindly change the description of the course for Intermediate who have previous knowledge on coding, but not for Beginners.... We struggled alot.

The files that are given in readings are unable to open and turicreate cannot read that files also. I cant complete my assignments without reading those files. They haven't given any detailed explanation about how to read those files. In videos they had explained through csv files but in assignments they had given sframe file which are unable to read

Dato tool does not even install properly.. so n´makes no sense to continue with the course. The support team fail to help in installing ... :-(

Uses very old versions of libraries. Many students are facing issues which remains unsolved. Not recommended to pursue it.

graphlabcreate creates many problem while working..it should be changed

Based on Python 2 libraries not working on python 3. Obsolete Course

Too old, bad packages, not much to learn. too basic.

Thoroughly disappointed to see this course based on

Too many videos and not cluttered assignment codes

This course is excellent for anybody new to machine learning and wanting to learn this new skill from the top down. For me, I have a strong background in machine learning, not in the context of big data, but I wanted to get familiar with Python and learn how modern companies are using machine learning in practice. This course provides that applied approach to implementing a broad range of machine learning applications with Python, applied to real problems.

A course this small cannot provide everything - what this course does not provide is in-depth technical tutorials on the workings of machine learning algorithms. There are many courses out there which do, but this course to great for learning a practical approach to problem solving with machine learning and data processing.

If there is a downside, I would say that the use of paid packages in the lectures (graphlab) limits the student's ability to learn Python using the freely available packages on the web, which was my personal preference. However, this is not purely negative, since there are many employers out there who would like to know that you have practical knowledge of things like AWS and graphlab. I did enjoy learning about those packages and services and I feel like I learned something positive which I can share with potential employers.

Overall, a very good concise course - one of the best on Coursera for vocational learning in my opinion.

Excellent overview course. It has exactly the right balance between explaining Machine Learning concepts, and providing enough supporting mathematics & logic to understand why these concepts are correct (without going through epsilon-delta proofs).

Having followed several Machine Learning courses, this is now definitely my favourite new course, replacing Andrew Ng's famous course here on Coursera (which was also very good & especially complete, but required too often a leap of faith - this course provides really more details on the "why"). Furthermore, the exercises in this course are spot-on: they use Python and GraphLab Create (for which you get a 1 year student license when taking this course) - the big advantage is that you can focus on the Machine Learning aspect, and not on how to implement something in Python (or Matlab or R). The exercises are challenging enough and require some thought, exactly what they should do. This is not a "look up the right answer in the slides" course when it comes to exercises, which I particularly like.

The chemistry between the teachers is also very nice and shows they just love Machine Learning, and love teaching it (which they do very well).

If you some familiarity with statistics (a bit) and mathematics (a bit of matrix & vector calculations), and want to understand what Machine Learning is about, then this is THE course for you.

Emily and Carlos, you are the best! Thank you so much for offering this great course. I like your humor, your casual, yet very direct and practical, approach of teaching.

I'm a film student from Germany but I was always interested in Machine Learning and AI - more like a hobby. This course gave me a very good intermediate understanding for the mechanisms behind this hyped and often overcomplicated subject field. The knowledge I gained helped me deliver a way better master theses in film school. I was able to (automatically) collected huge amount of tv-series data on several platforms via import.io and dbpedia and build a really great, combined database (dato's SFrame was very helpful here!). Through the techniques of this course I was able to push the analysis in my thesis a lot further than I ever expected!!! I will try to finish the other courses of this specialization although I'm an expert and professional in a completely different field. It's just so much fun and so comprehensible!

Also I got the impulse for a great sci-fi television series, which I will be writing the next few months now ;)

I came into this course knowing little bout Machine Learning. In fact, besides knowing a touch of HTML, I have no significant background in computer programming. Even before I started watching the first video, I was already expecting this to be an especially challenging course, for me at least. However, I was pleasantly surprised with the content and delivery - Carlos' and Emily's adorably dorky banter and their clear and concise approach to the various case studies made it easy for me to grasp the fundamentals of Machine Learning. Their delivery of the course's content is beyond reproach. (Although I would have loved to see Carlos going on a little more about Messi and soccer in general!). I struggled a little on the last question of the final assignment (Week 6), but besides that, it was smooth sailing. Overall, it was a positive learning experience and I'm happy to say that I now know more about Machine Learning than when I began. If you're new to it, this course is a great way to learn what Machine Learning has to offer.

Excellent content, and at just the right level for a getting-your-feet-wet-course. I especially liked the overall vibe of the lectures, which was relaxed and kind of goofy, and it's actually kind of nice to get some sense of personality from both Carlos and Emily. This is a topic of how to understand and manipulate the world as expressed to you through data -- a completely dry and theoretical approach would be tragic. I eagerly look forward to the rest of the specialization. And I had an ah-ha! moment in the week 5 homework -- it's a fairly simple model of building song recommendations, but when you actually look at the recommendations that come back from this algorithm, you kind of see that it does an intuitively better job than any system you could design and build without using ML techniques. Being a (successful) software engineer, this was both humbling to me and inestimably cool! It's not just a few new tricks to add to my bag-o-tricks, it's a whole new field to digest and investigate.

I'm very excited about this!

A fascinating tour of what's possible today with modern machine learning tools. The beauty and challenge of this course is the approach - diving right in to the tools to work through and experiment with some case studies. This is not a talk and visuals only course. You will be hands on.

This may be demanding for some, but is worth the effort. The course says no previous experience necessary in Python, but I recommend having at least completed a beginner's course before trying to tackle this. (Or familiarized yourself with Python if you have other programming experience - it has its quirks, like every language.)

The course will introduce you to the current state of play in machine learning and both show you what's possible and also where the limitations are. This is not a superficial course (talking points only) - you will learn enough to be dangerous. If you want to be a little safer, do the follow-on courses too. (At this time, only the 2nd course has run - regression - but it was very good).