The course is really good and would I highly recommend to others. This course builds upon your machine learning foundations really nicely. But I would say, anyone who takes this course must take other courses from this specialization as in this course we used only the existing functions for all he machine learning algorithms. So in order to implement those algorithm yourselves, the further course must be taken.

One more thing, there is a lot to learn in this particular course, which is a good thing, but can be heavy, specially if you're trying to complete the course in a short span of time (I had to do this in 2 weeks).

Loved that class. My only issue is that it uses GraphLab (which is Prof. Carlos Guestrin started, so there is some potential bias on the pertinence of that library) instead of a truly open source library like scikit-learn. As a consequence you need to acquire a license for GraphLab, which is a little tedious (and will expire). Also, their notebook system doesn't work very well (at least for me) and you learn ML on a platform that you will need to acquire a license for in the future (it is unclear how much it is).

That said, the material is very well presented, clear and exciting. Looking forward to dig deeper into it.

Fantastic course. Carlos and Emily are amazing teachers. The let down i had was that there were no proper instructions on downloading graphlab, jupyter notebook etc. Due to lack of guidance , i spent majority of my first week fussing about, downloading course requirements. I wasted a lot of time in downloading stuff rather than enjoying the course. Hence, i would recommend the course creators to include a small segment on how to download the requirements and into which file. Otherwise , this is a highly recommended course and the case study approach makes it more practical and relatable.

If you have a computer with Windows be prepared to the challenge of installing Turi Create package which is recommended in this course. Videos are not updated and use Graphlab, while all text instructions tell you to use Turi Create. However Turi Create only works with Mac or Linux. So you have to install Linux within your WIndows with WSL. It's a real trouble and not described in the text.

Also there will many errors you'll be getting when using commands for older packages (this commands in the course are just not updated).

Overall the course is good especially videos and speakers.

The course was great in terms of the content and Emily and Carlos were amazing Professors as I can see how passionate they are about Machine Learning. However, I am giving this course a four star because some of the materials are really outdated and does not fit with the current version of the software which makes it hard to connect sometimes. For example, in one of the quiz questions, the "correct" answer was actually not correct when using the data provided but it has to be selected to pass the quiz. I'd love to see a more updated/recent version of the materials covered.

This is a very awesomest course for novice learners. 👌. A little bit outdated but nothing an hour of google search will not solve. Use of SFrame was a bit confusing. I mean, I understood the difference between SFrame and Turicreate after many many lectures. it should have been explained in the first few lectures. And there is another confusion about the codes of graphlab which the videos contain while you code in Turicreate. It is hard and this is exactly why it is the best way to deeply understand the concepts. Going to study other courses in the specialization as well.

The course, as intended was just an overview, which sometimes can leave the student with a lot of questions. (which i guess was intentional too). Also i feel the instructors were trying a bit too hard to sound 'cool' and 'fun', or maybe it's just that i am 32 and i cannot relate to it much. The content was excellent and the course serves an ideal platform for the launch of the specialisation.

Kindly give other free options of software, since graphlab is paid. I small session on scikit, nolearn or any equivalent machine learning package would be extremely helpful.

I really enjoyed this course and found it fun to use the iPython notebook to play around with the ML models. The instructors were great, but I encountered some issues in the assignments for the last two weeks where the understanding that was provided in the lectures wasn't enough to pass the assignment. Since all the machine learning models are used as a black box, I think it would be beneficial to audit this course and then pay for the others in the specialization. Nevertheless, I truly feel like I have a good high level understanding of Machine Learning.

A very good introduction to Machine Learning Concepts & Principles. Easy to understand, while at the same time, giving a solid foundation on the models.

The only detractor is: usage of GraphLab - feels very custom and thrust upon. Definitely easier to use due to the abstraction inherent ion the programming constructs provided but IMHO, would have been much better to use more generic and popular packages like sci-kit, TensorFlow, etc.

Finally, the DL concepts in week6 felt a bit rushed and confusing. Admittedly, it maybe hard to abstract into a week.

Pros:

a) A good overview on machine learning, with case studies that are practical and can really help you remember what machine learning is about.

b) It requires Python, which is a language that more are familiar with than R.

Cons:

a) It uses a proprietary (and expensive) Python module. It's free for academic use, but if you want to go into data science startups, using GraphLab may not be as practical, as, say, pandas.

b) If you want a more detailed discussion, go for the next courses, or read an ML textbook.

Overall, I'm happy with what I learned.

Good introductory course. Provides hands on experience with toy examples that helps to better understand the content. Light on math so should appeal to a wider base. I completed the course in a couple of days. Lecturers are very good with clear exposition of material in most modules. The weakest part was discussion of deep learning but it is hard to fault the course providers since their aim was to provide an overview of the field. Really looking forward to rest of the courses in the specialization to gain more in depth understanding.

Loved the speakers enthusiasm and the breadth of ML applications and topics presented in this course. I particularly liked the practical approach it is base upon. I was a little bit disapointed by not using Open-Source libraries like scikit-learn, pandas, etc. (but this seems a recurrent comment made by others. I also spent quite some time struggling with various turi related issues (license, expired certificates,, etc.). Finally I regret the last 2 courses (5 and 6) were removed from the specialisation (and from coursera as well).

Pros: Good course organization, very good explanations, a nice introduction to basic concepts of Machine Learning, because the entire specialization is created by the same instructions it has good continuity and there is a sense of logical progression between courses.

Cons: The instructions on the assignments can be a little confusing for absolute beginners (but the forums are a very good source for help), the license to use the GraphLab Create framework for free for this entire specialization expires after just a year.

It was a great experience while having this course I learned a lot of innovative modules. The course was swift was not too rapid and steady I would definitely recommend this course for the aspirant who is looking for a head start in the machine learning career. I might have a suggestion for the mentors that the course python notebook are not up to date and tools used to explain in the videos varies with the python notebook this causes chaos in the user's mind and the user might get disturbed or distracted

I would've loved to give this course 5 star. I am very sad because i can't. The course material seems to be old and not exact when it comes to installation of softwares and packages like turicreate, jupyter notebook, anaconda etc. As a newbie i had a very hard time installing and getting a running ML environment. These should have better treatment in week 1, otherwise most student will loose hope and might not continue. But overall this was a great course. Thanks and Love for Carlos and Emily. <3

I thought it was a good course, interesting to see the perspective of some different ML methods that can be used. I think the biggest take away from the course is to use data and play around with it and the API's that are out there just to get a base understanding of how to play with them. Usually things have very good documentation that can be used as a help. I would recommend the course for people who want to just see what ML is about and play around with some python notebooks while doing it.

This is a nice entry-level course on Machine Learning. Emily and Carlos show their enthusiasm for ML and engage students in the subjects they are teaching.

The video lectures have a nice format in which you can see the professor while he/she is explaining the presented slides. This makes the course feel less online and more personal.

The course makes special emphasis on the practical side of the presented material. All the modules have a practical part in which you practice the theory presented.

Simple materials and clear explanations on concepts. A good course to begin with. However, students will be using graphlab (a python package for machine learning) in the entire specilizaiton instead of other common packages like pandas and scikit-learn. Beginners may feel a bit hard to implement some of the algorithms using those common packages. Also, if you choose to use scikit-learn instead of graphlab for the assignment, your answers may differ from which they use to grade the assignment.

This is a great introductory Machine Learning Course. The required knowledge is almost nothing, so it starts really basic, but got more interesting on the last weeks.

It focus on a practical approach. You will use GraphLab, a powerful tool that allows to easily start applying ML algorithms on real cases, even with just a basic theoretical understanding about why this works.

It made me think a lot how can I take advantage of this tools, and I'm excited about start working on some new projects.

This was a great overview into the history and current techniques in ML. I am an engineer in process control, so there are areas where I see this could be used. I like the fact that NN's found a place finally which is working well. Obviously the purpose of the course was not to delve too far into the maths, and as such "sells" the software which is a tad costly. Point however is the purpose of the course met the intent, and I found it really interesting and worth the time ! Thanks for this

The material in this course is very interesting. I feel comfortable with the concepts and algorithms. I am definitely prepared to utilize these skills in an entry-level manner - it will take some hands-on practice with real datasets to build expertise, understand the nuances of these approaches and expand my knowledge base. I recommend a decent level of comfort with programming. I completed the Python for Everybody specialization, but still struggled with the programming in this course.

I think It was a very useful course. The case study approach is a very efective technique to understand and practice the underlying concepts. However, i'd like to highlight maybe this quality might be affected because the explanations are using graphlab but the reality is the use of turicreate. Other thing: turicreate for windows is very restrictive, it would be good idea facilitate the installation and usability for this OS. Congratulations for the course and spec, I like it so far.

This course covers all basic machine learning tasks, including the new emerging technology like deep learning. The programming assignments are very interesting, face to real world applications, have an appropriate difficulty and are with good written guide. However, tools used in the course are SFrame and Dato Graphlab. If you are using popular toolkits including scikit-learn and pandas, you will be in trouble to set up the corresponding random seeds and fail to get the correct answer.

Good course to review with higher level application of Machine Learning.

The entire course needs to be updated and could easily be more concise.

Wish it was based on Python3.x, numpy, pandas or sci-kit, etc. What would be cool is a more database

oriented application so for example an AWS or some database integrated way (a more modern approach ) to implement, integrate, and deploy their case studies for linear regression, linear classification/classification, clustering, NLP etc,, etc.

A great introduction to machine learning with applications. However, there is still a small issue is that Graphlab is used instead of more commonly used Numpy and Pandas. I understand that Graphlab does have a great advantage in terms of not having to store large data into memory for some applications, but not directly learning tools that are actually used in the industry is still kind of a pain.

Also, RIP for the last 3 courses that never had their chance come into the specialization.