It has a lot of mathematics, however grad to the way the course is structured it is possible to learn as course progressing and the coding part is well elaborate helping non-math guys like me to be able to learn and understand the concepts.

This is a course suitable for people who have basic coding experience and would like to go further to learn machine learning. The instructions from Andrew are very clear, every single material is useful.

Great thanks to Andrew and his team!

I really like the approach to learning that Coursera offers, this was my first course, but certainly not my last. The best part for me is the practical application of knowledge, which is done at the end of each chapter in the form of tasks.

This course is just awesome. Andrew Ng gives you the underlying intuition of the two most popular supervised learning algorithms, linear and logistic regression. I got to understand and implement the mathematical models of these algorithms.

It was very nice ! The concepts are very well explained and since I'm familiar with calculus and linear algebra I had no problems understanding. Most of my work was concentrated to learn Python, but the practical tests give a lot of hints.

This course helped me to understand the basics of regression and classification problem and laid a foundation for me to keep learning and exploring the Machine learning and AI field. Thanks to Coursera and Andrew NG for helping me to learn

Very easy to follow along and understand while providing plenty of examples to make the concepts clear. The subtitles were inaccurate at times but apart from that it was a great course to set a foundation for my machine learning knowledge

Very well structured course, that presented in a way that builds your intuition for the subject. In addition to the excellent lectures by Professor Ng, the Jupyter notebook labs help to reinforce the learner's understanding of the topics.

Truly the best course for a beginner to get started in machine learning. The core basics are taught in a very fluent way which will help anyone with a little bit of knowledge in math and python programming get started in machine learning.

GThe BEST COURSE ! Must see everyone who wants to start learning ML/AI - Basics of Supervised learning, Linear Regression, Gradient descent, solving clasification and regression problems, Python implementation also with vectorisation :))

This course has helped me gain insights into ML concepts without requiring me to do any mathematical problem-solving. This course and the subsequent ones will definitely aid my PhD dissertation writing, SO I REALLY RECOMMEND THIS COURSE.

Absolutely amazing. Covered everything well and explained everything to a great extent. Learnt a lot from this course and I am very grateful to Dr Andrew Ng, Stanford Online and the Coursera Team for creating and maintaining this course.

I learned a lot with this course, thanks to the instructor Andrew Ng with Stanford's qualified course materials. I think I have laid solid foundations in the field of machine learning. It will be much easier to build things on top of it.

this course is really excellent. The theoretical explanations are really good. I could follow easily. The programming part is a bit difficult if you don't have experience in programming. A bit more teaching on this would be really great.

It was amazing journey with andrew. I have never seen such an amazing teacher in my entire life. He taught each concept mathematically and gave proofs which is why I love this course as well as teacher. Thanks Andrew and Deeplearning.ai

Great content and very well taught by the instructor. However I believe that video sessions for the programming labs could would have helped me better as it was sometimes difficult to understand the role of "function" of used libraries.

I was hoping for a slight mathematical understanding about the regression and classification in machine learning before enrolling for this course and today I am glad that I did enrolled. Amazing explanation and informative lab sessions.

Better than the original due to the interactive Jupyter notebooks written in modern Python, than the Octave environment on the previous versions of the materials. And as before, Professor Andrew Ng is an amazing and engaging instructor.

Great course, gives you a deep understanding of how linear, multi-linear, polynomial, and logistic regressions work. I just wish we got more examples and assigments using scikit-learn, instead of "building from scratch" the functions.

Very nice and well guided experience in this course. Thank you, Professor Andrew, for all your knowledge and help shared. Also loved the jokes and puns during the videos and that the codes and calculations were explicit. It helps A LOT

I took this course several years ago, and it played a significant role in establishing my foundational understanding of general machine learning concepts. It is impressive to see that the course has been consistently updated over time.

An excellent course, rich in content and learning. Andrew Ng has taught everything in a simple and effective way. Good learning experience. I would recommend it to everyone who want to learn ML from scratch without any prior knowledge.

Es, probablemente, el mejor curso que he realizado sobre Machine Learning.

Introduce de una manera muy sencilla conceptos centrales de la disciplina.

Mr Andrew Ng es un gran profesor, realmente he disfrutado mucho de esta experiencia.

Supervised ML: Regression and Classification on Coursera was excellent. The course content was clear, and the practical examples made complex concepts easy to understand. Andrew Ng’s teaching style is outstanding. Highly recommended!"

The explanations in this course are very good and I really managed to understand everything, which I think is remarkable by a topic like machine learning that is at least perceived to be very complicated. Thanks for this great course!