It is very interesting and useful course to gain understanding of Machine learning. I found it very useful to develop intuition of how ML algorithms work. Thank you so much Prof. Andrew Ng and Team for developing this course materials. I loved it.

Firstly i would like to thank Standford University for granting me financial aid for Supervised Machine Learning Course excellent and well oraganised weeks and all lectures were taught were clearly and even assingments were challenging as well .

Excellent course, a complex concept presented in a very motivating and simple way. Dr. Ny has the talent to keep you interested and curios for every concept included, is like a good movie that you want to watch from beginning to end.

Thanks Dr. Ny

This course is really structured very well and the concepts are explained in the best way possible by Mr.Andrew. The math behind the algorithms is explained very well and was easy to follow because of the practice labs after almost every lesson.

Andrew Ng has an amazing way of presenting the information and ideas relevant to machine learning! I took this course over my summer break, and I am glad I did, as I learned valuable skills such as the basics of linear and logistical regression!

This course is amazing! I'd been watching Andrew's previous specialization for a while and I really wished it was with Python instead of Octave. When I realized he - alongside his colleagues - was releasing a new course, I immediately signed up!

I like this course because you can actually impelement the theory you have learned in the lab using python.And also a very clear and enjoyable explanation by Andrew. I would recommend this course to any one who want to start in machine learning.

It's an absolutely amazingly designed and easy to understand course where there's enough information and materials to learn and utilize as the basics of machine learning. I can now say I'm very well versed in both linear and logistic regression.

This Course Designed for Beginers and also He explains the behind mathematics in simple way that makes me to understands the concepts and as well as the it works. in my point of view to start career ML go through is Course / Specializaton 😊😊😊

Great course, I had studied ML before in university but didn't understand the core concept and how does the functions really works under the hood, Andrew explained in an excellent way either the coding part or from mathematically implementation.

Excellent course!! The training was thorough, concise, and taught in a manner that was easy to absorb. Andrew's friendly manner of teaching helps to make each aspect of the subject matter a bit softer around the edges than it might otherwise be.

I recently took this course and found it to be an excellent introduction to the fundamentals of supervised learning. The course is well-structured and provides a solid foundation for understanding key concepts and techniques in machine learning.

Explicit and intuitive explanation. The most incredible part is the setting of the corresponding jupyter notebook files which serve as both practical demonstrations and notes. I would give 5 stars to the course and keep going to the next course.

It help me understand some topics I was confused about, such as how backpropagation actually worked rather than just using it blindly . I also learned the importance of cost functions and choosing a good alpha parameter to find a local minimum.

Great introduction to prediction models using the mathematical formulas, plotting data and exercises to give you deep comprehension of basic concepts of machine learning. you will need just a basic knowledge of python sintaxe and data structure

Coming from a health science background, I found this course very useful as it was able to guide me through the basics with very lucid explanations and examples. Looking forward to completing the specialization and learning a lot of new things.

Beginner friendly, thank you for this course. I have a civil engineering background with no or very little programming knowledge and about ML and I never had to refer something outside of this course materials because of lack of understanding.

The explanations were simple and easy to learn, and optional labs were also very helpful in understanding the concepts studied during the course. Looking forward to further courses in this specialization and also deep learning spacialization🤓

I was searching for a practical and theorical course and this course has everything i wanted, the teacher is incredible good at submit clearly the information and complicated statements are explained in a manner that all people can understand.

Absolutely loved this course. I knew the basics of Machine Learning, so this part was a great reminder for me. Also, a big thumbs up for writing everything by hand - it let me understand gradient descent theory much more than imported library.

This course is great for beginner, but before learning anything on this course you must have some math knowledges about linear algebra and calculus in order to understand deeply about machine learning algorithms. Besides, everything is great.

This course is perfect for people wanting to get started with machine learning. The approach to the topics is clear and easy to understand and almost no prior knowledge in machine learning is needed. Highly recommended for complete beginners.

Very well structured theory and explanation, but the lab part is not very elaborative and easy to implement.

The codes are written u just have to run them and then later in the test, writing and implementing the code and logic gets difficult.

Prof. Andrew Ng is an amazing instructor with rich experience. I would like to be grateful to the entire team behind its realization. This course provides a balance between the theoretical aspect and the programming aspect. Highly recommended

The course covers detailed parts of explanations. Sir Andrew Ng and his team has developed this amazing course for learners. Algorithms are taught along with statistical content which can be hardly seen in teaching methods of any instructor.