Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

Although this is an introductory Machine Learning course, it covers all the important aspects and all the common algorithms, the programming skill is not so necessary with Matlab, a production-ready software, or its free software clone Octave, even if it is needed GPU acceleration. This course was much easier than I expected, even considering the lack of unnecessary mathematical theory, it was teacher Andrew Ng who made it possible.

Hello this is Sparsh kaushik and i just completed my ML course offered by Stanford.

To be honest this course offered exactly what i was looking, a proper Introduction into the world of Machine Learning.I really appreciate the efforts of prof Ng for making this course and all the mentors who guided me throughout the course.

Overall this course is a must if you are new to ML and want to dive into this beautiful word of data.

This is the most interesting course that I had ever taken. I want to thank my tutor Andrew Ng for teaching those wonderful things. This course even changed my career path and also made me superior to my teammates by knowing these cool stuff. I have also designed some applications using the concepts I had learned in this course. Lastly I am grateful to my tutor @Andrew_Ng for make profession life more fruitful.

This course teaches the fundamentals of Machine Learning very well. Although the use of Octave can be questioned, the exercies generally served as a good addition to the lecture videos. Aside from minor complaints like not editing mistakes from the videos in some occasions, i am very satisfied with this course! Perhaps Prof. Ng could reshoot the lecture videos to improve the video quality in the future.

This course is a really amazing and well taught course.Andrew sir is really a very good teacher and he made the complex topics quite easy to understand with his cool examples.Moreover, the optimization techniques and advises given for debugging or improving the machine learning program were really helpful. I hope I can take full advantage of this course and build up a career in machine learning.

Fabulous course for starters of machine learning.

Even without a solid foundation of linear algebra, calculus, statistics, one can still have a good sense of machine learning by professor Andrew's patient teaching. And the coding exercises helps me a lot in comprehending some concepts that are unclear when watching the videos.

Sincere gratitude for the teacher Andrew. Real respect.

This course was splendidly awesome. I am in my final year from grade C engineering college in New Delhi, India. This course helped me a lot to understand about the basics and gave me deep understanding about the field. Thanks to Andrew NG Sir who made this great website for students like us and such an best class content. Highly Recommended to all!

Thank You!

Great Course to start with Machine Learning. All algorithms and technical expects are covered and explained in detail. Assignments are well framed and gives you a proper intuition on algorithms use and performance on real world data. Also appreciate the advise on debugging and optimizing algorithms on small test examples, before running on large data sets.

I am currently a PhD student in math. Over the years, I have spent countless hours using many different online resources to learn many different subjects. I can confidently say that this course was, by far, the best online resource I have ever used. Thank you Professor Ng for putting so much work into carefully explaining these complex topics!

Hi Andrew,

I liked this machine learning course so much. I enjoyed doing all the assignments. I am working in IT industry. I was thinking of working in a new age technology and then my brother told me about this course as I love maths and programming.

I am looking forward to get deep knowledge in machine learning.

Thank you,

Asmita Patil

Amazing course for beginners who love challenges. Certain parts of course require lots of thinking and advanced math skills, and others are so complex that you just have to trust the teacher with it. But, in the end, you learn a very practical and useful skill that you can use to improve your and others' everyday life.

Very eye-opening for a person with a very little knowledge of the aspects and maths behind machine learning. The exercises were somewhat difficult since it's been 15 years since my last maths class and I really haven't been doing it since. So a lot of effort really needed, but once you've done it, it feels great!

I loved this course. Loved the way of teaching and how well structured it was. I can only recommend using the programming test cases when doing the exercises. I wanna thank Prof. Ng and the Tutors of this course. It has been a great experience. If I had the choice, I would purchase this course again, no doubts!

There are a lot of courses about Machine Learning, but this one is very comprehensive and it also goes deep to the theoretical base, so you do not rely on ready-made libraries, rather you have to write your own code in GNU Octave. I highly recommend this course for everyone who wants to do ML as her career.

Great comprehensive way to break into machine learning as a subject. I feel confident that this has provided the foundation I need to further explore the subject either by reading up on new topics in the machine learning area or thinking about how to apply machine learning concepts to my personal projects.

Found this course extremely useful to build up the basics of machine learning and its application on the real world. Quizzes and programming exercises were very helpful and apt. Enjoyed a lot while learning these complex algorithms. Thank you Andrew sir and the Course Era team for creating this course.

It was my first time, that I have to come in contact with machine learning. This course helped me pretty much to understand the basic problems. It was a very good introduction, very well explained by Prof. Andrew Ng and I can realy recommend doing this class not only for beginners. Thanks for that!

Fantastic introduction to various aspects of Machine Learning. I particularly liked that Prof. Ng shared a lot of practical tips for real-world ML applications beyond just the technical content. It's partly due to this that the course is still up to date ten years after it was first released.

Majored in actuarial science so machine learning is not a new word to me. Some of the techniques are very simple such as linear regression. Andrew is so clear and organized which helps me figure out a line in between. The projects are pretty easy but sometimes need plenty of time to debug.

This course was fantastic! I can honestly say that I learned more in this course than any course that I have ever taken. It was challenging, but doable with time and effort and well worth it! I would recommend this course to anyone interested in a rigorous foundation in machine learning.

Awesome course,

Before completing the course I was lacking in the concepts and if I refer any documentation or some opensource I couldn't understand it well. After completing the course I'm familiar with the algorithms and I understood all the concepts very well.

Thank you Andrew NG.

If you want to learn how to apply machine learning in a wide range of practical settings, this is the course to take! Professor Andrew Ng obviously put a lot of time and care into developing this course and preparing the excercises to provide you with a really smooth learning curve.

Tried different youtube tutorials before, but this course was the one, that gave me true understanding about machine learning and how it basically works. I'll always recommend this course to start a machine learning journey! Thanks to Andrew NG for this course and for his effort!

Excelente curso! No solo explica detalladamente el sustento teórico de los diversos temas, además propone ejemplos aplicativos sencillos que ayudan a una mayor comprensión. Y por si fuera poco toca temas algo complejos pero sin perder la excelente pedagogía.

Lo recomiendo!

Andrew Ng teaching is great! He breaks complex concepts into easier to consume chunks. He not only explains how to implement specific machine learning algorithms, but how to recognize when there are issues with the models you create and how to address them.