I have completed this course in full and as a result, I am highly satisfued with how Professor Andrew Ng explains the materials. Thank you for this! However, I cannot understand, why after completing the course a part of studying materials are not accessible, even though I paid a sufficient price for the course. These unaccessible materials include Python programs which were used as a practice. Frankly, I find it unfair, since this practice would be extremely important to revise the materials while improving my skills in Machine Learning in the future. Moreover, a part of the montly fee was paid also for the practice materials. I may agree that these Python programs can be private, however,there should be ways to overcome this issue. Without the possibility to revise the code it will be much harder to create our own applications and programs.

tldr The course is a great introduction to ML for an audience already comfortable with mathematics and Python. For what it aims to achieve, I think it does a great job. /tldr

The mathematics involved in the first course of this specialisation is not that difficult if you already have a solid foundation on calculus. Some functions used in the Optional Labs are called for you from already written python scripts (which you have access to, and can download to inspect). The first 3 weeks (and probably the rest of the course) will not teach you fundamentals on Python or mathematics or statistics, and some details regarding the choice of loss function for logistic regression were omitted. Furthermore, libraries such as scikit-learn were used to complement the material, but not explained in depth. (Granted, this course is not about Python libraries.)

All in all this seems like a great introduction to ML for people already comfortable with mathematics and Python.

If you already have the foundations required (Undergrad basic calculus, Python) you can do all 3 weeks in one day fairly easily without distractions.

One of the best courses out there on Machine Learning. Clean, Crisp and up to the point. Short but delivers all the things one need. More better than a classroom program. Saves one's time and energy.

In general, I think it was a valuable course to take. I like the way Andrew tried to conveying the ideas intuitively to make sure the students understood the methods behind the learning algorithms. However, I would've loved if there was more in-depth treatment for the Math aspects of the obtained results. Also, the assignments + Optional labs were not as engaging as I hoped. What I mean by that is, it almost required no deep thought from our side to implement the procedures. In other words, there was a lot of skeleton code that makes you "implement" the algorithms with almost no thought (which I don't think is beneficial to the student's learning experience)

The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.

It is the Best Course for Supervised Machine Learning!

Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!

The quizes are too straight forward and simple. The code exercise too short as well.

Also disappointed that vectorisation is introduced but cost and loss functions are still calculated in for loops.

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

It's completely fine. I have learned a lots of thing in this first course of specialization. Thanks to courseera for giving such a good and fine course on financial aid. I am very thankful to them.

Excellent content. I'm a math guy so I would have enjoyed some more in-depth theory, but that's what books are for I suppose!

I've been using Python for a long time now so understanding the code was nice and easy.

Thank you for your hard work putting this together!

The course is good but once you cancel the subscription, you lose access to the codes. I think that should be change.

Teaching is an art and Andrew Ng is a great artist. He explained everything in the course in the details and with examples easy to comprehend. Thanks a lot for helping thousands of students like me.

I have just finished the old machine learning course, and I'm doing this because I'm learning python/numpy/matplotlib. I thought the question during the course and quizzes insulted my intelligence. The material is great, but you need to improve the simple questions and quizzes. The first programming assignment was too easy, the second programming assignment was at a fair level. I still think more should be left to the student to do.

I started with onld ML course last year, completed successfuly but did not purchase the certificate. As I am more familiar with python than Octave, this new course make thing clearer for me.

The explanation is clear, and all of the source codes provided in each jupyter notebook show a clear visualisation of how well the model learns or fits into the data when a parameter changes.

if labs were optional then why are there compulsory coding assignments, labs must not be optional, instead make us type code step by step, like MATLAB onramp courses.

First too easy and at the last assignment no chance to get help for weeks. I can't finish this course. I don't see any difference from the hints towards my programming lines but still it's not working so I can not finish.

Not a good course for beginners!!!! It should teach Python programming or have it as a prerequisite for the course. There should also be projects for the course

Unable to Open the labs and submit the lab assignments

The best of the best. I am superglad to see the upgraded version of the legacy Machine Learning Course by the super helpful tutor, Andrew Ng, implemented in Python. Very detailed Labs, allowing plenty of practice and intruition. Luckily enough, I was already great at Python and NumPy. I hope the Labs won't be intimidating to a Python beginner.

Overall, this course deserves more than 5 stars. It is second to none, as far as my exposure to Machine Learning is concerned. Thanks Deeplearning.AI and Standford for creating such a fantastic course. I am definitely taking the remaining courses in the specialization😊

Professor Andrew can explain complex knowledge clearly. The Python lab can help learner to understand algorithm. The course is more valuable. I am excited to learn the next course for advanced ML.

Excellent balance of theory and practice provided by exceptionally well documented and visualized examples and code in Jupyter Notebooks that one can interact with to build intuition.

Pretty good introductory course! Personally, I would like to see more time devoted to the Scikit-Learn implementation (and maybe Pandas data frames instead of NumPy arrays for the training data) as opposed to hard-coding the algorithms and using really small data sets. Scaling upwards and using those libraries on larger data sets should be relatively easy after you nail the foundational concepts in this course, though. There is definitely something to be said about knowing the mathematical algorithms running in the background of these black box models, and this course does a really good job of explaining them (namely, cost functions and gradient descent).

Apart from scripting these algorithms in Python code, the course is somewhat lacking when it comes to conceptually explaining regression and classification models. For example, there is no time spent explaining how to interpret regression model coefficients and intercepts, and there is little time spent explaining the probabilistic interpretation of the sigmoid function and the importance of choosing a good decision boundary. It is one thing to know how to program these models and another thing to be able to explain them to people without a technical background, which I think could be a good lesson in future versions of the course.

Overall, great introduction to the models and their implementation in Python! I would absolutely recommend the "optional" labs throughout the course (especially if you're new to Python) because they show you the code that you'll have to write in the required assignments.

The course content is great but I didn't like that the instructor said that the labs are optional and you don't even have to know python and then I found out that there are graded labs!! this is really confusing as even when I tried to use hints and write the code I found out that it does not work.