excellent course, the exercises could have been more extensive but overall it's very good. I recommend carefully going through all labs . It will help you not only understand but implement code yourself. Also if you are not familiar with multivariate calculus and linear algebra, you should do that first to get better understanding of what's going on.

It is a very good course for the beginner to start learning ML. It includes learning of regression algorithms like linear regression, logistic regression, and regularization techniques. It helped me a lot to understand these topics. I would like to thank Stanford and the team for making available this type of courses with the option of financial aid.

An amazing and comprehensive learning experience and a great way to start with machine learning from ground zero. Being someone with a limited experience in the Python language makes it a bit challenging but still accessible. I really appreciated Andrew's calm teaching style and his great attention to detail. Thank you. Looking forward for course #2.

for someone who knew about ML as much as you would know about the universe from the pop science books, I think this course really dove into the subject with real math and implementations. I can now write python code that can get the program to learn solutions on its own! it almost feels like magic. :)

Thank you Andrew for being such a great instructor

This is an excellent course in ML. Andrew Ng is a brilliant instructor who motives the theory with fun real world examples. It is however a bit of a leap of faith to classify this as a beginner level course. The prerequisites to be able to follow along confidently are a crash course in Python together with undergraduate level algebra and calculus.

Its a very good course. I knew nothing of ML but after doing this course I am pretty confident that I can implement any supervised machine learning algorithm from scratch. Its beginner friendly. If you are afraid of mathematics and machine learning, you should take this course, because all your fear would be gone by the time you finish this course.

This course is designed to help you grasp the basics of all machine learning models and the math that goes with them. By the end, you'll have the knowledge to work with common regression and classification models. In short, it's a highly recommended course for anyone looking to understand machine learning fundamentals and apply them practically.

Explicaciones claras y buen material de laboratorios para acercarse al contenido mediante experimientos y visualizaciones. El único punto del curso que podría mejorar son las tareas de programación: considero que tener una mayor cantidad de ejercicios a realizar, quizás más breves pero más frecuentes, haría más fácil asimilar el contenido dado.

Love, love, love it! Concepts are very clear, simply explained and easily understood for such a complex topic. Albert Einstein once said: "if you can't explain it simply, you don't understand it well enough."... and the course instructor is an embodiment of a true master of his craft. I'm going to complete the remaining courses. Thanks Andrew!

Course is really very well designed! Andrew is a great teacher and I am impressed by how easy it is to follow step by step in concept and able to do the coding in lab even without python programing background to start with. Will definitely recommend to everyone that is interested in Machine Learning, I am looking forward for the next course!!!

Andrew's lectures gave me an insightful and accessible introduction to the intricacies of machine learning. His wit and positive attitude motivated me to accelerate my learning progress. I am truly grateful for the opportunity to partake in such an excellent and rewarding course. I am ready to take on the second course in the ML Specialization!

Such a beautiful course I have ever seen about machine learning. No, one can explain like andrew Ng sir . He explain all the algorithm with mathematical aspect too. I can solve all the algorithm with or without sklearn library. Thanks for making these type of course.It is help to make a perfect root of student in the feild of machine learning.

The course is very good since the topic really explains the theory behind the concepts needed for machine learning. Andrew Ng also discusses the concepts very well and the lab assignments are very helpful to solidify the ideas you have to learn from the tutorial videos. I would definitely recommend this course, especially for beginners in ML.

Even auditing it, you learn a wealth of material from the videos. In fact, in some respects, not having access to the optional labs (you have to "upgrade", or subscribe, to gain access) motivates you to create your linear regression code from scratch. That makes it a bit more of a challenge and helps you work on your coding skills as well.

Professor Andrew NG and his team did a great job yet again with structuring this course. Coming in with some background in Machine Learning, this course for me served as a great refresher for the introductory concepts in Machine Learning. I'm also delighted to take baby steps into python programming and scikit library through this course.

Learned a ton. It's a bit to take in. Unless you have a math background I recommend taking the Mathematics for Machine learning course from Deep learning.ai. Anyway I can recommend this course to anyone who's trying to break in to AI. The instructor is the best. And the content is very well structured. Thank you so much DeepLearning.ai Team

Andrew does a great job of simplifying complex topics into digestible bites for the student. I have taken other ML courses on another platform, and there, the instructions were merely how to use canned algorithms. I did not learn much there. This course explains some of the math behind the scenes and thoroughly explains the how and the why.

After completing supervised machine learning, I get acquainted with fundamental of machine learning and learned about regression and classification algorithms and many other features and how to apply them on over projects. Of course, I want to keep on learning and reviewing materials and learn other courses to be a machine learning engineer

Andrew explained difficult mathematical equations in a manner that make it easier to understand the concept of those strange formulas and actually how they were developed. I believe Although we may not use those formulas in real-world problems but understanding the concept will help us a lot in understanding any machine-learning algorithms.

Muy buen curso! Logré aprender las herramientas básicas de conocimiento sobre regresión lineal y logística para luego continuar profundizando de forma independiente la librería scikit learn y otras que automatizan los procesos, pero es fundamental saber qué es lo que está pasando al interior! Muy buen entendimiento de los conceptos básicos.

I found this course to be incredibly informative and well-structured. The explanations of both regression and classification techniques were clear and easy to follow, making complex concepts accessible. The practical examples helped reinforce my understanding, and I appreciated the hands-on projects that allowed me to apply what I learned.

Prof Ng does a fantastic job explaining complicated concepts in a simple way. The coding requirements are not too egregious and help to solidify the concepts for longer-term retention. I felt that I learned the basics very well, and could furthermore explain them to someone else as well, which is helpful in my work in Product Management.

Concepts were taught in a very visual manner right from the basics which made them crystal clear to me. The optional labs were amazing and helped me visualize the topics covered really well. Having a basic knowledge of Calculus, Linear Algebra, and Python is very helpful to fully understand the formulae for models, but it is not required.

Course was very interesting to follow and definitely broadens the mind in the machine learning subject. Instructor walks through the concepts of supervised machine learning with understandable examples and great teaching techniques. definitely recommended. Thank you Stanford and Deeplearning for providing me financial aid for this course.

this course was really helpful in order to make a person acquianted with the concept of machine learning and it's uses. But it can be better, maybe its my mistake but in optional labs if a person delete cells unintended, there is no way for recovery. So by solving this bug i believe this course and similar courses would be more efficient