I think the lectures are very good. They contain enough details and explanations to understand the fundamentals with a lot of graphs to accompany the data. I don't think this should be understated because graphs are so fundamental when trying to understand data. I also thought the labs were very helpful for experimenting and coding at your own pace.

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!!!

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

Awesome course. Lots of high-quality material to explain the concepts. Optional code labs to gain intuition and both quizzes and practical labs to strengthen what you've learned. Also, the updated curriculum puts high emphasis on real-world tools (Numpy, scikit-learn) and concepts like regularization, feature engineering, feature scaling.

This is my first Machine Learning course and I am happy that I successfully completed it. This course is very beginner friendly. The course instructor explains the concepts with great simplicity. The optional labs are very helpful. It felt awesome throughout the course and I am super-excited to join the next course of this specialization.

Great and easy to learn experience throughout the whole course ,every part was explained in details. This is not an criticism ,but I had slight problem understading the gradient descent of logistic regression. Needed to take help from your old videos in youtube. But most of all Thank you for this course,can't help to continue the second