Incredible course. Instead of completing it over 3 weeks, I took the time to simutaniously practice coding side projects whilst learning the concepts, and also took the time to catch up on my math skills. This in total took me 2 months, with a few breaks due to a lack of time. I absolutely recommend this to new learners, and also do recommend working on side projects like I did, alongside this course.

This is really a great course. Andrew Ng showed really great understanding of the and he was able pass it on by breaking the topics into atomic units. The labs were helpful and the quizzes were easy also. However, I would suggest that a complete project should be given and the whole code should be written by learner which can ensure they course was fully understood and further enhance their portfolio

Excellente formation, qui nécessite d'avoir quelques bases en Python et en mathématiques, mais qui présente parfaitement les problématiques de la régression et de la classification. Les quiz intermédiaires sont relativement simples (à condition d'avoir écouté les vidéos) et la programmation reste limitée à des fragments de fonctions, faciles à implémenter dès lrs que les concepts sont bien assimilés.

I am truly amazed by Andrew Ng's Supervised Machine Learning course! The content was incredibly insightful and well-structured, making complex concepts easy to understand. The practical examples and exercises were invaluable in enhancing my skills. This course has been a game-changer for my understanding of regression and classification techniques. Thank you, Andrew Ng, for your exceptional teaching

In my experience, the course is of great value. However, I believe that incorporating additional programming assignments every week would enhance the learning experience. This approach would allow learners to put into practice the knowledge they have acquired from the video tutorials and solidify their comprehension of the concepts, thereby reducing the likelihood of forgetting them in the future.

The "Supervised Machine Learning: Regression and Classification" course by DeepLearning.AI and Stanford University on Coursera is an outstanding introduction to machine learning. Led by Andrew Ng, it offers a perfect blend of theory and practical application, making complex concepts accessible. Highly recommended for anyone looking to build a strong foundation in supervised learning techniques.

Very well organised and delivered. Build the foundation of ML and then goes to details of Logistic Regression and how to overcome from the problems when your model doesn't perform well. The programming assignments are really good and you don't want to miss that as it is when we see the workings of Logistic Regression. Also, you get the opportunity to learn from the veteran of ML, Dr. Andrew Ng.

One of the best courses I've ever done. I love how it touches on first principles, but without getting bogged down by them - I feel I've come out of it with a well-rounded understanding of and ability to implement in Python the core topics - but perhaps more importantly have been inspired by the friendly and enthusiastic (and super-clear!) delivery style to keep going and dive further in. :-)

This course was really good. The visualizations in the lab were really creative and insightful. By the way if felt like in the third week, the speed of teaching stuff began to increase, It was ok but i was shocked at first. I am a teacher myself, so i realize how much this team worked to prepare this content. I want to thank all members of this team one by one. I hope i can meet them soon. :)

The course was very beneficial and helpful for my professional career. I am very happy to have time with this great course, very thankful to you for providing this course It will be helpful for me to play role in the service of humanity. I will serve the people with my career with the aim of welfare of people so I am looking for more courses for my career to become Machine learning Engineer

You may consider telling me that you can write the code in the quizzes in your way like when I was doing these quizzes I wanted to write it with np arrays broadcasting and vectorization [I did that anyway] but it was very confusing that there is a template of z_wb and f_wb inside nested for loops and I have to stick to this hierarchy. Thanks for the course, it was amazing and informative.

The course focused on learning how to do the equations and programming for the algorithms. It gave a good understand of not just the algorithms but when and how to implement them. There was no time wasted on being concerned how to generate plots or gather test data for the assignments. That was done for us... which was a great help. All in all a very well planned and executed course.

Beginner-friendly discussions and covers both implementation via Python, the concepts, and the math behind. You may hear that this course is overrated, but they forget that this course is designed to be as accommodating as possible for beginners like me. This course encouraged me to study more about machine learning, almost feels like finding a friend in an ocean of condescending people.

I really want to extend my thanks to all the professors, engineers, and more generally all the staff who already involved in the process of making this complete and amazing course. I've finished the course 1 recently and it really taught me the basics of Machine learning, encouraging to start course 2 and continuing this fascinating way of learning algorithms, ML, and AI. Thanks a lot :)

Had already completed the old course "Machine Learning". Took this course because of the switch from Octave to Python. So i thought it was a great idea to repeat what i had learned and at the same time sharpen my skills in Python. Really liked the enhancements, like the extra optional labs with Scikit. Also this was a preparation for me since i intend to take the 2 continuation courses.

This was an exciting ride, I enjoyed every bit of it. The explanation, the presentation, the examples, and the labs were up to the mark. I loved the optional labs, the way they were structured, and the way they explained every block of code is worth appreciating. Thank you, Coursera, and Stanford for providing such a fantastic course, looking forward to completing the specialization.

Terrific introductory course, but I wish it gave you the option for more hands on implementation of the supervised machine learning algorithms as you progressed. I could have easily passed this course with knowing the bare minimum, but I wanted to become proficient in the foundations, and unfortunately there wasn't much in the way of testing your knowledge without the training wheels.

I felt after learning this, that my overall understanding has become very deep and now i feel very confident about implementing this in real life scenorio. It has given me clarity on "how to steps in Machine learning" . Very intutive and natural course for topic of vast calibre and application. Thanks to the team of coursera, deeplearning.ai and standford for sharing such information.

This course is fantastic, everything from the previous course but more. Adding Python instead of octave/Matlab is excellent, and the programming assignments are also beneficial. The teaching is exceptional as always. If you are looking for a course in machine learning, this is the best pick. I enrolled the day the course was released, looking forward to completing the specialization.

Very good - easy to understand instruction and enjoyable to listen to.

The lab's are excellent to take the theory and test it. I found using the labs was the best way to understand the maths and logic, and how the layers of iterations come together. Particularly in the last few lessons when you have operations happen on individual features, individuals examples and then the whole set.

Andrew Ng is a great teacher.The material is very well presented and Andrew makes sure the learners develop an intuition on the concepts of the course.As a side note i found the assignments too easy, but i can understand the philosophy of the course is not to discourage people but help them understand the concepts and give inspiration to enter the exciting world of machine learning.

Andrew Ng is one of the pioneers in the field of AI. His original course, while very theortically enriched, was showing its age for the choice of its programming language. This new specialization was just the right thing for someone like me who needed to implement all the concepts in the de facto language of AI, all the while learning the said concepts from the leaders of the field.

It is an introductory course, but it manages to go a little deeper than I expected. Obviously, do not expect any (or almost any) mathematical proof or deep explanation about veery topic, but you can expect to learn the mathematics behind logistic and linear regression, being able to implement then even without a package. Andrew's explanation about the logic of the models is great.

this course was so impressive to me , from building up the intuition to practical labs as well as the optional labs , I have visited the concepts provided in that course before , but the way I understood those concepts in this course was new to me and added a lot to me , thanks for providing such valuable information in a very simple systematic way. Special thanks to Andrew :D

This course is a very good start for beginners in the field of ML and AI. Concepts are explained very well by Andrew himself. Best part of the course is how Andrew tries to develop the intuition behind different algorithms. I had been reading some books to understand these concepts but it was hard. Now, I think, I have the foundation to understand different books on these topics.