Not as good compared to the previous machine learning course (the one done with Octave), you aren't pushed to write the vectorised implementations, even the example code and starter code in assignments is done with loops. No introduction into the "@" syntax of numpy. I prefered the math syntax of the prev course with hypothesis function and Theta for weights, including adding the bias term itself as theta_0 within the weights vectors.

The educational material was good, but the assessments were very simple. The quizzes and assignments could have been more thorough to properly assess whether I had grasped the material, rather than "fill in the blanks" and multiple choice (though often only 2 choices) style problems. A certificate from this course is not a good indicator that the student has learned the material.

The course dives too deep into the math behind the type of regressions to make predictions. But it lacks practice in using python libraries to actually put them into use. Instead of having to calculate each operation manually it should present the learner with more opportunities to implement python machine learning libraries to get some hands on experience.

Error that was shown after an assignment is done was not helpful to debug on where the error is and took couple of hit and trial to work. The cell blocks said all tests successful but was unable to submit the assignment. User friendly text explanations on where the error is, Highlighting in red etc could have been helpful.

The videos were great and it was an excellent introduction to machine learning. However, I feel like the quizzes should test our knowledge a little more. The questions are too simple and makes it so that I am not sure if I actually have a good understanding of the material being taught.

Practice labs are not well organized as previous course. The optional labs have less hands on activities which gives less hands on activities for the learner. It would be great if there could be more activities that the learner could perform instead of just looking at the code

It's okay for intro but I feel need in more practice. Don't expect to get skills from this course. You will get a lot of new theoretical information with just a scratch of practice. So I suggest to mix this course with some good books + practice.

it was a really great theorical course but the practical parts were too small. so I know alot about the concepts and I can calculate stuff but i can not implement them that much , and I'd rather the course to have more difiicult math! well done!

Could give a bit more focus on the coding part. What I mean is rather than giving notebooks with already written code, you could teach the code to the students and have them write it themselves.

It is really interesting, I think I have miss more focus on connecting everything together from the function f_wb to cost function to deltas to preditions. The labs are not very challenging.

Code cannot be transferred easily to jupyter notebooks or google colab. The course was 90% theory. Needs more real-world practice and projects. Other than that Theory was very good.

the course is well structured and you'll gain the fundamentals of ML. But the practical labs should be more challenging in my opinion. I am looking forward to the next course.

There is very less practice content for students. I hope keeping more graded notebooks would be helpful for students to get more practice and understand the concepts better.

The course is interesting but focuses on very theoretical aspects, more suited to academia than the working world. Unfortunately, the audio is not the best.

This course requires Python programming experience using the libraries this course needs. It is not suitable for learners with basic coding experience.

Much more of the lesson content needs to be focused on the code, rather than the underlying maths, seeing as it is purely code which is assessed.

Love the trainer. Would have been nice to start coding earlier. By the time we were called to actually write code, it was way too late.

The videos were really helpful, but the optional labs were useless. Besides, the practice labs felt like you are jumping few steps.

Doesn't really enable "DeepUnderstanding" - all abit rushed. Spend time afterwards going through more examples with more rigor.

Had problems with Python. I does so much for you it sometimes makes it difficult for a C, C++ or Java programer

Lectures were very clear and smoothly explained but the discussed consepts were only for complete begginers.

Thank you sir . I learned many new things from this course which will help me to much in my research work..

Good for absolute beginners with no background. Too slow and peace-meal information for anyone else.

Lot of great learning, but not taking into account using a lot of newer open source options.

Everything mostly taught was theory.I learnt very few concepts of coding.Disappointed!!