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.

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well. There are various techniques devised for the same. Traditional machine learning algorithms have been applied in many application areas. Researchers have put many efforts to improve the accuracy of that machinelearning algorithms. Another dimension was given thought which leads to deep learning concept. Deep learning is a subset of machine learning. So far few applications of deep learning have been explored. This is definitely going to cater to solving issues in several new application domains, sub-domains using deep learning. A review of these past and future application domains, sub-domains, and applications of machine learning and deep learning are illustrated in this paper.

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!!

The only way these courses could be better if there were far more of them from the same professors. If more of the nitty gritty details of these algorithms were fleshed out in all their glory, more algorithms, more mathematical derivations & more tutorials in the programming languages & libraries used. Otherwise, these MOOCs are near perfection. A very, very nice introduction for beginners with just a little bit of math & not too much programming. Just enough for busy people. I've reserved that 5th star due to the slow pace that the MOOCs have been released (which will presumably be irrelevant for future machine learners) & the fact that there really needs to be more of these very high quality moocs. So there aren't enough of them, so I reserve a star. Hopefully in the future that will be irrelevant as well in which case I'll regret not indicating 5 stars.

This first part of the specialization course provides a gentle, well-balanced introduction to ML concepts. I had some partial knowledge about the subject and some familiarity with the statistical techniques, but the thing I needed most was some real-world requirements and applications. And I am very content with what is presented. The cases are concrete without being complex and while the course is careful not to drown you in theory, it does slowly build an insight into the techniques being used. Former Python experience is recommended, yet even if you have never used Python before, I think you can build the necessary amount of skill during the course. And I must not end without saying a couple of things about the instructors: They are amiable and entertaining as much as they are knowledgeable and that makes it a very pleasant experience.

Overall I thought it was a useful introduction to ML. I actually liked the fact that I needed to install WSL on my Windows laptop to run Turi Create. This provides me access to other programs and compilers that I usually access on Linux servers (e.g., gcc, gfortran, etc). I am fairly familiar with Python, but tend to use Spyder. Becoming more familiar with Jupyter has also been a bonus. One thing that I was not as familiar with was SFrames and the use of dictionaries within Python. I have mainly used Python for numerical linear algrebra, image processing, signal processing, and the like. Links to good tutorials for SFrames would be helpful. As an introductory course, it focuses more on applying techniques rather than specific details related to the techniques. Hopefully, these will be discussed more in-depth in the courses which follow.

Very good course for an overview of Machine learning .

It is very good first step for Beginners. Word of caution : Course lecture videos uses the Graphlab. Course jupyter notebooks have the up-to-date Turicreaatecode. You can also use Panda/Numpty/Matplotlib to arrive to same conclusion. However I am new to ML/Python so stuck to course guided notebooks. it worked for me.

Course first gives you an intuitive and solid theory background and then walks you through how to implement that into Graphlab/turicreate.

Best part is Quiz / Assignment. You are not just copy pasting code from the class but really challenged to find a solution using turicreate API .

Overall good start. Recommend with foot note that be ready to do some googling on Turicreate while following along the course.

Overall, an excellent hands-on course to learn the basics of machine learning. I am really glad I chose this over many other options available online.

If I were to pick straws, a couple of the programming quizzes could be better, especially week 6 images quality and related questions 2 and 3, and couple of the theory quiz questions were misleading.

Finding all the iPython notebooks and data online on Amazon was a Godsend as those data files just refused to upload from my system. I wasn't aware that these were available so I could have saved 2 weeks of delay in completion due to data upload problems.

I am keen to continue my association with Univ of Washington and Coursera based on this experience though I am not in the market for certifications at this stage.

Great introduction to Machine Learning with the case study approach. Gives you a quick preview on what Machine Learning entails iwth practica use cases without making you learn a new language and several frameworks before you write your first line of useful code. The course seems to emphasize heavily of the practical uses of machine learning

The instructors have a pretty fun way of interacting with the viewers/students. Instead of reading power point slides. The quality of the content is great, though it can be made a bit more consistent in a few modules. The labs are pretty great and an important part of the course material.

The format of this specialization should serve as a template for future specializations made on Coursera.

The Course is well structured and gives a good overview of Machine learning and it's various applications. By working with case studies, it is easy to understand the logic behind the application. The only regret is that the usage of GraphLab means that it is a bit difficult to transfer the learning to something more open-source such as sci-kit. Graphlab does most of the algorithm work for you so you might not really understand the backstage working of it. Hopefully the courses further in the specialization will teach what is happening behind the scenes. Also, all the data is cleaned already so it is very easy to work with. you will have to learn data cleaning and data mining separately to be able to use it in your daily work.

Brilliant course!

This course it taught by two amazing professors- Carlos Guestrin and Emily Fox. In the very first session itself, they make it crystal clear that they love what they do, and in the subsequent videos they show you why you should get excited as well.

The course puts remarkable emphasis on the concepts rather than raw code, and once you're prepped on the fundamentals, you're gently introduced into the code. This to me is a wonderful way to go about a new subject.

The slides are simple and clean, the work of a brilliant team shines through the entire time. Carlos and Emily are really funny and you just fall in love with this course because of these two exceptional teachers.

Without a doubt- highly recommended.

Since when I got to know coursera, I have been going through plethora of machine learning courses .I couldn't complete even one of them as the pace was too boring and I couldn't connect anything to the real world scenarios. So I had paid a huge sum and joined a course on classification model using SAS near my place. To my astonishment, this course by the Amazon professors far excelled than what I took up earlier and I feel I had wasted money to pay that institute. This is the best machine learning course ever which has the theory and practical stuff at the same place. Thank you Amazon professors for all the help. Hope I start actively participating in kaggle competitions once I finish the other courses too.

Great introduction.

I have two suggestions:

1- GraphLab is a great tool but it is commercial and it is not available in all companies. Hope there were some guidelines, slides with sample source code etc. for each session showing how to complete assignments with an alternative free library.

2- In the slides, I found very useful practical hints mentioned quickly by the lecturers (e.g., how to fit a better regression model by using the same feature with higher order such as X^2, X^3, X^4...). There is a high chance to overlook such hints since they are mixed with other materials. It would be nice if such practical hints are gathered and published in a separate document like a cheat sheet for future references.

The instructors are amazing and teach at a pace, which is slow enough for things you are new to but fast enough for more familiar things. I had decent machine learning background but none of SFrames. However, I had no difficulty understanding SFrames from the word go. The assignments are the best part. If new methods pop up in the programming exercises, step by steps instructions explain how to use them and you learn even more that way! (some of the hint/help links in the assignments did not work for me). Instructors used a lot of examples during lectures but I felt that theoretical part was lacking depth (may be it is me because I always like to see more maths :D). Carlos was great so were his shoes :D

This whole specialization is an outstanding program: the instructors are entertaining, and they strike the right balance between theory and practice. Even though I consider myself quite literate in statistics and numerical optimization, I learned several new techniques that I was able to directly apply in various part of my job (ok, not in the foundations course, but in subsequent courses). We really go in depth: while other classes I've taken limit themselves to an inventory of available techniques, in this specialization I get to implement key techniques from scratch. Highly, highly recommended.

FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

This course was not very simple to understand for me, because i'm not a native english speaker and my english knowledge is academic; but thanks to english subtitles and transcriptions of videos i was able to keep pace with teachers as they talked and thanks also to the excellent learning material. The mathematical concepts explained are clear and concise, lessons are not lost in learning theoretical ramblings, but everything is set for the application of the acquired concepts in real life.

Python is very simple programming language to learn, but if you want to deploy a real machine learning application you must study other aspects of the language that, rightly, are not covered in this amazing course.

This is a fun course that gives you a very good overview for different machine learning methods. It is indeed a case study approach which is very nice because you get an idea of how versatile machine learning and its application really is. Plus, you get to solve some meaningful questions using machine learning yourself. This first course doesn't explain how the algorithm works in detail but on a higher lever so that you understand the underlying idea and principle.

The videos are super fun to watch as Carlos and Emily are super likeable, and very engaged and excited about the things they're doing and teaching. Thank you so much, I had a great time doing this course!

I took the 4 (formerly 6) courses that comprised this certification, so I'm going to provide the same review for all of them.

This course and the specialization are fantastic. The subject matter is very interesting, at least to me, and the professors are excellent, conveying what could be considered advanced material in a very down-to-Earth way. The tools they provide to examine the material are useful and they stretch you out just far enough.

My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning. I hope they get to do that some day.

The course gives an excellent overview of the main types of algorithms in Machine Learning. The lecturers are both very clear and I like the combination of annotated lecture slides and jupyter notebooks.

My only problems with the course were with the coding/assignments sections. Because of the choice to use GraphLab, I was forced to install a virtual environment with Python 2 to be able to run the jupyter notebooks myself. I understand the choice and agree that GraphLab is a very intuitive and easy to use module, however if it is not going to be updated for Python 3 then the coding sections should be re-recorded using a different library.

This is one hell of a course

This is basically a good introduction to each and every basic ML technique, what makes this distinct from the other courses is that, you really get your hands dirty with writing code on Python, which is really an industry relevant language for Data Sciences and ML

The theory is also, taken in detail and the assignments are cleverly designed, this course was a joy to take. Initially, I thought that I would complete this course in a span of 6 weeks, which is actually prescribed. But, the fluidity of the course content was so great, ended up completing the course within a week

Definitely, taking the advanced courses

I appreciate this course that is one of the best mooc courses I ever done.

Being a computer science engineer, I found the "hands on" approach particularly amazing, it lets you immediately start applying the topics with real use cases.

I started this course after completing the Machine Learning course of Andrew NG (Stanford University), and this made a perfect companion.

I think that if I didn't have followed the NG course, probably I would miss something from the theory, particularly maths and statistic that in this course are missing. (in any case where superbly covered by NG course)

Thanks a lot for the course

Stefano Priola

Turin Italy

Excellent Introduction to the Machine Learning applications. This is course does not cover all the "details" of the method, but rather just prepare/motivates the use machine learning. Only criticism would be that course relies on GraphLab Create, but frankly speaking this is quite alright as I mentioned that the point of the course is to deliver the motivation and show some interesting applications of machine learning rather than explaining every detail of each module. If you want more details, take the entire specialization where you have more freedom to write your own code without the use of the use of Graphlab.

Excellent introductory course on Machine Learning. The material is taught at a level that does not require much in terms of pre-requisites, both in terms of the math and the programming requirements. From my perspective, I have an extensive background in Math, and some background in programming (MATLAB, R). I had not used Python prior to this course, and I found that I could keep up and learn both some Python and ML. I was able to finish the course in two weeks. Well done to the instructors who made the videos fun and accessible. Recommended for anyone who wants to learn something about ML.