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.

The focus of the whole course is on gradient descent. I guess it is needed for some other algorithms but here we could have just found the derivative. If I had no background in math and statistics I would give up ML seeing this.

This course is helped me a lot . I gained some skills related to the supervised learning .this skills i learned in this course is very helpful to my future projects , thank u coursera and andrew ng

Andrew Ng is the best proctor for Machine Learning. The course has been perfectly balanced with thoritical as well as practical aspects. After this course I feel so confident. From ZERO to HERO

absolutely amazing course, coding assignments are designed perfectly and the course helps in understanding the working and the math behind the algorithms which makes it so recommendable.

a great course to understand theory of supervised machine learning. Need lectures for numpy and scikitlearn

Too much theory and very few practice.

I feel like this course tries it's hardest, that everyone can follow it. But because of that it doesn't really dare to go deeper than just give an overview of machine learning. The tests are way too easy to pass with 100% and you can't really write your own algorithms afterwards. Also most of the examples are with one variable, so it's easier to follow, but it would be much more helpful, if we could see more complicated and real live examples.

This was really a math class not much at all about machine learning. There was some abstract example that most of the detail about were hidden making it really hard to understand what was being done other than learning formulas. I would not recommend it.

the labs arent useful + absence of a lot of supervised machine learning models

THE FINAL ASSIGMENT IS TO HARD

This is the best learning lesson to pave the path on AI engineering . Prof.Andrew NG is just wow and intelligent. The way he had taught every basic things with examples is the epitome to this course.

Excellent course. Intended as a refresher, and had a better understanding of feauture engineering, scaling, and logistic regression. Good hands on labs were very practical, engaging and rewarding.

Really enjoyed the course, had a few questions by the end of it that were resolved quickly in the forums. I would implore others to use them too as they are a great resource.

Andrew Ng is a very good professor, he explains complex concepts in a very simple way and with the help of many visualization and graphing tools. Highly recommended course!

The course is very interesting. I have learnt a deep understanding on machine learning, now I know the difference between regression and classification.

Good, I keept working on these codes and searching for clues in videos. Good structure, reinforcment of some knowledge.

Andrew Ng is a God of ML. No one in this world can make this course more easier than him.

Though the concept was thoroughly explained, I find that it lacked materials for learners who prefers to read the course after in order for me to understand it better. There were no slide handouts nor was their a step by step tutorial on the coding. The coding was spoon fed to learners so it was hard to figure out the assignments for the coding. Syntax for the coding was not also thoroughly explained nor a handout for the syntax. It is not a good course for coding but a good course for understanding what your are computing and how you go about the problems.

This is basically a theory ML course disguised as an applied course. There are labs where you can see code be run, but there is no annotation describing what most of the code is doing or why it is written in the way that it is. Very frustrating and almost impossible to apply in real life. It teaches you the formulas for writing linear and logistic regression models, but not how to write the code architecture to get you to the point of implementation or testing. It does a good job of teaching ML vocabulary and the basics of regression. Ultimately, I would recommend you be at least an intermediate or expert Python user before taking this course. Beginners can take it, but they won't be able to take anything from it.

Too much content per week. You learn the basics of the formulas, but it is difficult to digest all the information in only three weeks and with practical exercises in Python if you have no practice in the language.

The logistic regression part really needs rework, It is not so clear for an engineer student and is very confusing at some parts

Tried, but unsatisfied. Hence, I cancelled the subscription within a week as specified for a full refund. However, there is no way I can get hold of anyone from Coursera. It is a scam system. You have been warned.

I had completed my course and why I did'nt get my completion certificate?