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

It uses turicreate which is not available for windows .

It is interesting, but turicreate isn't compatible with current version of python (3.8) and there's little/no support as the forum is not curated and not much student interaction. The problems seem to be only loosely related to the material. Many questions in the problems aren't discussed in the lectures and turicreate isn't widely used so it is difficult to find explanations or clues on how to proceed.

I think this course is outdated as they are using python 2 also the platform they use for machine learning only supported by python 2.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!

T​his is out of date. I cannot get week one to work.

Content is not updated, 3 years old, they tell you that the course will cover TuriCreate but all the videos show GraphLab content. Syntax is for Python 2, and although this last one is not supercomplicated it shows the lack of interest for the students from the University because after 3 years they haven't updated the content!

Go to the forums and see how many people are stuck in Week 1, just trying to install the tools requested, which require plenty of workarounds to be installed in windows.

The teachers are stiff and, although they say the course involves a case study approach, you start with the basic Python assignments that will not apply to the cases. Also, they push a tech that does not seem to be the most commonly used one in Machine Learning. I wish Coursera would put a date on these courses so we can see when the courses are older, and therefore expect the content to be outdated.

Please mention that users need to use turicreate and sframes etc "only" to complete the assignments.

it is wrongly mentioned that one can complete assignments using any tool but very first assignment is forcing us to use turicreate and sFrames !!

They are using python 2, It is very out-of-date.

After the first week, I count not pass the first test...

too old documents

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).

Presenters start off kind of silly and made me wonder what I was getting into. However this class quickly evolved to be 100 times better than the course offered by U of California on Big Data. You do actual python programming through a lot of serious concepts in data analysis, visualization, and machine learning. This first course is hands on - just use the libraries. They lean heavily towards Dato which is not open source - using a 1 year trial license. However there are better instructions and support for open source in subsequent courses. Also - the second course in the series which I'm taking now is taking what we did in course 1 and diving into the math and algorithms involved - walking through actual proofs etc. It doesn't require you to know them well enough to do on your own, but they do walk you through them and explain extremely well - you actually implement the resulting algorithms. I'm fascinated by this course and can't wait to apply what I've learned.

Very approachable for a beginner trying to learn a few evenings a week. It has a consistent pace and the topics are explained in the right level of detail.

Modules are centered around a Real world problem that is easy to relate to such as product recommendations on a website or analysing text. While other courses dive straight into calculus and theory where it's hard to recall the actual problem being discussed, this course doesn't have any those issues. It is very well structured and gradually progresses while providing real learning along the way.

The assessments are just the right length and offer a suitable challenge without expecting hours of work for each question.

A large benefit of this course is the environment setup is straightforward. I had my jupyter notebook running in a few minutes. With other courses, I was spending hours trying to install things before trying to learning anything but with this course I was up and running quickly

Artificial intelligence been around for long time and machine learning is the application to self learn through the data and apply and predict, be more and more accurate. This was a first encounter for me to know how deep learning and deep feature works! Probably, this was the time when I felt going back to university days and relearn few concept of statistics, in order to understand few prediction model and the usage. I was amazed to see and unaware of the fact, I am benefitting as user and million of users unknowingly. Every field and every industry and most importantly every area of our life is going to improve/ impacted with Machine learning. It is a great effort by the faculties, to bring such complex topics to level where it's looks like story telling and making folks understand through small assignments but surely it is a result of deep thinking and hard work which makes this course so interesting and intuitive.

Loved the material and the course design - it really works for people who don't have much time but want to understand the main principles of machine learning. I think I've watched every week's videos and completed assignments within about 2-4 hours.

The only suggestion I would have (and it is a very personal opinion) is to spend less time on illustrating slightly irrelevant aspects of the material, such as showing quite a few Amazon products or going through a full shoe collection. I can see how that can make the course a little more lively but for a person who treasures every minute of their free time, it can be noticeable, especially when it takes a significant fraction of the very well-sized small videos. This was a very minor issue but I thought I'd share in case someone else felt the same way.

Overall, a huge thank you to Carlos and Emily for a great course!!