Great course, instructor Andrew Ng does a great job, he offers explanations for beginners and also addresses the math behind (up to some level) for more advanced students, provides intuitions for several topics which make the student to understand better beyond only having the formulas displayed. The practice labs include visual aids (plots) and examples that allow the student to reinforce the understanding of each topic.

Excellent course, I've been dabbling in Deep Learning analysis for the past few months but wanted to gain a better understanding of the whole process and elected to take this course. I strongly recommend this for anyone who wants to dive deeper into machine learning algorithms, this was a wonderful experience getting familiar with all the math behind the algorithms themselves and I'm excited for the following two courses!

A best course for beginners , intermediate students. It's great that it contains labs on which one can work, play with it .

Labs are visually good to help understand concepts better and teaching by course instructor ANDREW NG is absolutely nailed to the ground.

Last but not least a recommended course to watch if you aspire to become ML engineer

--Thank you Andrew Ng sir and whole team who contributed in designing this course

After working in the industry as a web developer for one year, I made the decision to transition into a career as a machine learning engineer. I chose to take this course as my initial step in my machine learning journey, and I have not been disappointed. I have learned all the fundamental concepts thoroughly, and I must say that Andrew Ng's teaching methods exceeded my expectations. Thank you for making this course.

Very Detailed Course on Supervised Learning, Andrew Ng's Teaching is just so intuitive and simple to understand. The concepts are taught such that beginners can easily understand them... Anyone who wants to build their Carrier in ML or AI, must use this as a starting point after learning basic coding in Python and learning some libraries like Num-Py, Matplotlib, etc. Thanks for giving us such a wonderful course sir!

This course is by far the best I have ever taken (and maybe the best I ever will!). Andrew Ng makes learning an enjoyable experience, breaking down very complex concepts into simple and straightforward terms that really stick. I always looked forward to his next lecture. I am more than satisfied and happy to have completed this course. I now feel I have the foundation necessary to start building my own ML projects!

I am amazed at the ability of the instructor to convey the intuition behind the methods and at how much effort and though has been put into the materials for course. The mix of coding and methods strikes a perfect balance for anyone with basic background to get started. I sincerely enjoyed this course and absolutely recommend it to anyone, no coding experience, is needed and some basic math background will suffice.

Andrew Ng's "Supervised Machine Learning: Regression and Classification" course is an excellent introduction for beginners. Ng explains complex concepts clearly and concisely, making them easy to understand. His step-by-step approach builds a strong foundation, and the detailed labs provide invaluable hands-on experience, reinforcing the theoretical concepts. This course is ideal for anyone new to machine learning

This course is excellent for anyone looking to learn Machine Learning in an enjoyable manner. I found it very enjoyable to learn from this course. Dr. Andrew simplifies machine learning by providing intuitions and examples for each topic. I am grateful to everyone who contributed to creating this course. The labs were effective for hands-on learning and getting practical experience. Overall, it was a lot of fun!

Abseloutly amazing course, Andrew NG did an amzing job on teaching and also explaining concepts and formulas, it could get better if you shared some liks of usefull material about the theoretical machine learning for example I'd like to know why we use the linear regression model as the variable of the logistic regression model(sigmoid function). thank you for this amazing course sincerely Amirhossein Abolhasani

Hi all,

I am researching object tracking using machine learning techniques. This course gave me a clear understanding of Machine learning methods such as regression and logistic regression. The lecture videos and assignments helped me to think and understand clearly.

I would recommend this course to beginners who want to understand machine learning concepts clearly in regression and logistic regression.

Thank you

Great explanations for all of these difficult mathematical formulars. I understood everything, but there is definitely  enough to repeat over and over again to really get it . Need a little break before participating in part 2 and 3, but I´m looking foreward to it!

I absolutely loved the exercises, it helped a lot to test and try out things and to see, how easy some parts really are, if you take them separat.

Excellent course. Thanks to organizers, managers, lecturers, developers, etc. It was very interesting, very funny and very helpful. I am a senior data scientist and delivering ML lectures in different universities. In spite of that, I have found this elementary course quite valuable. It is always recommended to rethink known topics and ideas and see how other specialists are delivering those important concepts.

Thought it was great and felt it was much more beginner-friendly than the previous course. The programming aspect of it can be tricky if you've never had programming experience, so I highly recommend you learn the basics of python (variables, for-loops, functions, etc.) before taking the course. If you have some brief background in ML and programming you should be able to finish this course relatively quickly.

Very cool course, Andrew is not only a brilliant mind but also a natural teacher. After I have taken both this version and the previous (Matlab) version of the course, I feel that the assignments and quizzes are easier than in the previous version. However, thanks to the numerous Jupyter Notebooks provided, there is plenty of opportunity to dive into the matter in some more depth, and to learn something new.

It's a great course for the beginners in the area of machine learning. You should have some Python basics. Optional labs are great and you can learn a lot alone, if you have desire to investigate a bit their implementations. Final graded labs are also not so hard. I would maybe add more question to quizes and more exercises, examples and datasets. But it's, as said, still a really great course and thank you!

It was a delight for me to take the "Supervised Machine Learning: Regression and Classification" course lately, and I must say that it was a really educational and engaging experience. I couldn't have asked for a better introduction to the field of machine learning than what I received in this course. The instruction was provided by Sir Andrew Ng, a famous specialist, and was nothing short of extraordinary.

Andrew Ng is really POSITIVE teacher😭😭 the way he teach us is just like truly motivative and encouraging, even the course has a "heavy" math since the beginning. I really recommend this course for people who start to learn ML, but just be carefull because you really need a STRONG INTUITION about Graphic system, Gradient, derivative and ABSOLUTELY a solid-basic UNDERSTNDING about LOOP & function in Python.

Para mí ha sido una experiencia apasionante. Me ha parecido una formación muy precisa, fácil de entender para alguien que quizá no conoce demasiado de IA y con contenidos muy inspiradores. La plataforma me parece exquisita. Todo el material está fácilmente accesible y Andrew Ng, además de un referente en el desarrollo del mundo de la IA, demuestra además sus cualidades para enseñar y motivar a sus alumnos.

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.

One of the best and most comprehensive courses in the market with no true alternative. Grateful to Andrew Sir for designing such an advanced course for beginners in Machine Learning. This course includes well-structured optional labs, offering excellent visualization to enhance understanding. These labs are the true highlight of the course, and skipping them would be a mistake. Highly recommended!

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.