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Вернуться к Машинное обучение

Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

4.9
звезд
Оценки: 156,360
Рецензии: 39,993

О курсе

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....

Лучшие рецензии

ZL
6 дек. 2015 г.

The course is well organised, with cutting edge knowledge ready to use in our information era. And Andrew was really decent with clear illustration and explanations. I really enjoy taking this course!

AA
10 нояб. 2017 г.

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

Фильтр по:

176–200 из 10,000 отзывов о курсе Машинное обучение

автор: Shanthan K P

25 мая 2020 г.

A very good course packed with the fundamentals of ML. It has given me a great overview of what ML is and the assignments were well organized. Neil Ostrove and Tom Mosher were very quick at replying to the queries and were very helpful!

автор: Tony X

5 июня 2019 г.

Quite good, suggest for beginners. There is no much mathematics knowledge deeply involved.

Andrew Ng used a simply way to describe machine learning algorithm. It's really helpful to understand the concept.

Thank you very much!

автор: John C

5 июня 2020 г.

I will just simply say that this course was awesome! Prof. Ng broke down the ideas very nicely and “de-mystified” the area of machine learning. I highly recommend this for anyone who is just getting started in this area.

автор: Glenn B

21 окт. 2015 г.

Absolutely brilliant course and lecturer (simply brilliant Andrew!!!)

So precisely spoken; such brilliant tutorial notes, wiki, forum (mentor Tom Mosher - Thank you)

This course is better than many paid university courses.

автор: Amine M

11 апр. 2019 г.

I took this class to recap my ML knowledge. It filled up my ML knowledge gap! Anyone can take this class, regardless of background or level. There is always something new to learn in each lecture and topic!

автор: Seth W

9 нояб. 2020 г.

Excellent course, highly mathematical overview of how introductory machine learning models work. Thanks to Andrew Ng for putting together a lot of great material and challenging quizzes and exercises.

автор: ALTANAI

31 авг. 2020 г.

A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.

автор: Caleo M S

23 мая 2020 г.

Um curso incrível com uma ótima didática e exercícios que realmente estimulam o que foi aprendido em aula. Sem dúvida é a melhor fonte de conhecimento para adentrar no mundo de Máquina de Aprendizado.

автор: Juan J G P

25 окт. 2016 г.

Great course. A progressive discovery of the maths inner to the learning algorithms. This course gives that insight many ML practitioners don't have and is so important for making real use cases work.

автор: Omri M

16 янв. 2019 г.

terrific course, good balance of both high and ground level teaching. Good, hands-on experience. I actually appreciate the fact it isn't python or R - this way it's not geared toward a specific crowd

автор: Hejmadi P B

16 сент. 2020 г.

Loved the course. Andrew Sir explains the intuition behind the concepts really well. Excited to continue with the rest of the courses by him on my way to becoming an AI Engineer.

Thanks a lot, Sir!

автор: Muhilraaj A R

2 янв. 2020 г.

Thoroughly enjoyed by doing this course.Gained lots of Knowledge on machine learning and practical skills on applying it.Thank you Andrew Ng sir,Standford University and Coursera for this course.

автор: Luís R

16 янв. 2019 г.

The course has an adequate degree of dificulty. It is not easy. But, the subject matter demands for that specific degree of detail if we really like to actually do something with machine learning.

автор: Sai G K

19 янв. 2019 г.

This is a great course for someone looking to learn Machine Learning from the ground up. I would suggest this course to everybody from beginners to professionals. Andrew Ng is an awesome teacher.

автор: Jag S S

9 июня 2019 г.

Excellent course, everything is taught from the scratch. Anyone from any background can learn a lot about machine learning through variety exercises, tutorials and lectures. Highly recommended.

автор: Harshit S

6 июня 2019 г.

Very Good Course to start into machine learning, It uses Matlab which is very useful, all mathematics behind different algorithm nicely explained by instructor, Instructor is very good teacher

автор: Mohamed M K

11 мая 2020 г.

Really amazing course, Andrew Ng is one of the most successful professors in the world, not only he briantly teaches ML/AI, but he also does it with great sense of humility. Thank you Sir!!!

автор: Jody R

14 февр. 2021 г.

Professor Ng is a great teacher. I learned very quickly from his easygoing style. The content helped me understand the machine learning problems much better. Thank you very much Andrew!

автор: Tu V N

3 нояб. 2019 г.

This is one of the best online course I have learned over many years. Thank you very much Prof. Andrew Ng. Highly recommended for whom want to learn about AI & Machine Learning subject.

автор: Gil B

6 июня 2019 г.

The instructor gives simple explanations, yet covers all the topics deeply. The coding exercises are well designed and teach you haw to write machine learning with no past experience.

автор: Lakshya G

2 янв. 2020 г.

Really well defined course on Machine Learning. It would be ideal if you have some background knowledge on Math. Do Linear Algebra from Youtube ( Linear Regression) as a compulsion.

автор: Nathan M

5 июня 2019 г.

I thoroughly enjoyed the videos and programming exercises. I think Dr. Ng has great insights that will help me approach future ML problems with greater understanding and efficiency.

автор: Camille C

18 окт. 2020 г.

This class was really interesting and the videos are very well explained with examples to show how machine learning is applied in everyday-life. I would definitely recommend it !

автор: Nicholas J P

5 авг. 2020 г.

Amazing introduction to machine learning. Broke down complex topics in a very accessible and interesting way, looking forward to using the knowledge I gained here going forward.

автор: Brian

7 авг. 2015 г.

It's amazing, I can learn fanstastic stuff through this free course. There is no boundary. I could implement the machine learning code , and understand well. Thank you so much.