I can confidently say it's an outstanding learning experience. Andrew Ng's clear instruction and well-structured curriculum make complex topics easily understandable. The hands-on programming assignments and real-world case studies cement the learning. While some areas could be more challenging, overall, it's a highly recommended course for anyone interested in diving into the world of supervised learning.

A great introductory course into the world of AI! Much gratitude and thanks for the Financial AId. Before being approved for the aid, I tried other free online courses from d/t source, and I can 100% vouch this is the greatest. I really liked how Andrew presents the lessons, and even more how the course provides a solid understanding of the concepts in a detailed yet understandable way to a newbie like me.

A great course with well clarity of concepts. I have nearly no knowledge of most of the maths or statistics topics required for Machine Learning (such as Linear Algebra, Calculus, etc.) yet with time and determination along with some research of my own. I was able to fully understand and complete the course. The course breaks down certain aspects of Machine Learning very well making them easier to absorb.

I found this course very detailed with clear understanding of fundamental concepts of supervised machine learning. After a topic is explained there are labs for better visualisation of the topic. Optional short quizzes are present in the videos which help us introspect our understanding of the topic explained in the video. Practice labs are a great way to revise what we have learned in a collective way.

With some Python experience, this was super hands on and easy to understand. I came into this course without a strong knowledge of how to decodify math algorithms, and with Andrew's super clear explanations and the super hands on optional labs, I found myself able to see how the alorithm was changing through visual graphs, and become able to apply the machine learning mathematical algorithms into code.

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.

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.

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.