Rather simple introduction to Calculus in machine learning & data science. The course covers core concepts and linked well to applications such as Optimisation and Gradient descent.

The instructor provided good graphical visualisations to help learners understand and develop intuition on the concepts covered.

I actually understand gradient descent which is awesome. I need a little bit more practice running some of the problems to be proficient and remember how to do them, but I was able to complete them for the course and I suppose in real life I can just have my code (stolen from this course) find the answers.

An excellent course indeed, although with some caveats.

I love the simplicity and the examples of the instructor. However, sometimes the materials needed to be more complex and exciting to watch. I definitely recommend this course to absolute beginners in calculus or someone who needs a refresher.

Content was extremely helpful but assignments were too simple. If you could make the graded content a little more challenging it might really help the student push himself further.

We had a great time learning this course. We really had some good sessions with friends while learnin this. We found about new concepts. Thanks

Easy to follow for beginners. Concepts are well explained. I wish the Newtonian method had been explained in more details though.

The last programming assignment has some problems. If you touch the optional part, you get 0.

Knowledge very useful, however as benniger at phyton, programing task are quite hard

It was so hard and challenging. I've nearly cried and somehow i passed. Thanks :")

Too easy for exercise, but the video lession is good, focused on ML perspective

It great course but the numpy will be new for some one who don't know to code.

Labs could have been more difficult. Otherwise a good course for beginners.

Videos and explanations are great, but labs are rough.

Helpfully and covered all the topics related to ML

Good course and gives a good understanding!

Notebook test case sometimes crashes

You are perfect Platform Coursera

Nice for review.

Good Professor !

The material was great and very informative, and intuitive. That said, there were a few times when 1) the lab didn't align with the learnings - multiple variables were used in labs before described in later lectures. 2) The final quiz asked questions about using Newton's method approximation recursive formula, but the language used was very different than how the concept was taught in previous lessons. If mentors/contributors can look through the lessons/labs/quiz and ensure there is fluidity, that would be very helpful. Thank you!

Much time spent on Python in the labs that did not result in learning Python.

The labs make it clear that Python is VERY important, but I suggest a separate Python course to be taken first (even before the linear algebra material). Even making it into a four-course specialization is a far better use of one's time than struggling with labs that only8 illustrate, not teach, this critical tool.

Good content giving insight to the mathematical foundations of Machine Learning.

However the Python is horribly outdated, back at 3.8; which will sunset in a year and a few months.

Some of the examples are not PEP 8 compliant.

That aside it is good to really understand from where the machinations of neural networks derive.

a little bit overwhelming. I still dont feel like I understand everything. I wish it was more interactive. especially with writing codes and mathematics. I was stuck with many questions and felt demotivated.

Useful for beginners and fundamental concepts. Before starting the course I thought that the course will be helpful in professional journey but I found out that the basic concepts are discussed.

Videos: great, easy to follow

Labs: not so great