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Вернуться к Машинное обучение

Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

4.9
звезд
Оценки: 144,007
Рецензии: 36,513

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

RD

Mar 31, 2018

Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days

VB

Oct 03, 2016

Everything is great about this course. Dr. Ng dumbs is it down with the complex math involved. He explained everything clearly, slowly and softly. Now I can say I know something about Machine Learning

Фильтр по:

201–225 из 10,000 отзывов о курсе Машинное обучение

автор: Mohammed M

Apr 03, 2019

I learned a lot from this course, very recommended

автор: Yuhang T

Jan 17, 2020

thank you, I have learned a lot from this class.

автор: 个a

Apr 02, 2019

excellent class.worth your time and thank you ng

автор: Roei B

Jan 18, 2019

10/10. Andrew is an amazing teacher. Thanks!

автор: zhaoyi

Jan 02, 2020

Very good intro course to machine learning

автор: Zilin L

Jun 07, 2019

几乎没有数学要求,老少咸宜。

编程作业设计非常用心,专注于让学生完成核心人物。

好评!

автор: Hamed B

Jun 05, 2019

THE BEST COURSE IN ML BY FARRRRRRRR

автор: Bhanuprasad T

Jan 01, 2019

Loved it. Easy and Excellent Course

автор: Luu V L

Aug 06, 2020

best ML course in the world !!

автор: Jaspinder S V

Aug 08, 2015

Awesome course for beginners.

автор: Mulat Y C

Feb 14, 2020

Machine Learning

Data Science

автор: Mewada A J

Aug 06, 2020

best experience of learning

автор: 梁驰

Feb 08, 2020

喜欢吴恩达教授的课,讲的非常的好!教授很谦虚!赞赞赞!

автор: chandan k

Jun 06, 2019

Great course to study!

автор: Eugene M

Jan 04, 2019

Very useful course!

автор: Joy F Y

Aug 07, 2015

It's very useful

автор: Pavel K

Jun 06, 2019

A great course.

автор: Hacker O

Jun 17, 2019

very good!!

автор: Stephen M

Jun 05, 2019

Very useful

автор: ylfgd

Jun 06, 2019

very good

автор: THIERRY L

Jan 04, 2019

Excellent

автор: Saiful I A

Aug 07, 2015

Very Nice

автор: Vivek K

Dec 13, 2018

Awsome

автор: Lichen N

Aug 28, 2019

深入浅出

автор: Armen M

Apr 09, 2020

THIS IS A REVIEW FOR BEGINNERS

ADVANTAGES OF THE COURSE

When I remember myself deciding whether or not I should take the course, the questions that concerned me the most were these ones.

1. Since I am a beginner in this field, will the course work for me?

2. Did this course get outdated? (For those who don't know, the professor uses Octave)

3. In the end, will I feel like I can do some Machine Learning projects all by myself?

For those who have the same questions, here are the answers for you )

1. Yes, the course will work for you even if you are an absolute beginner like I was at the time (I did not know any linear algebra), It does get annoying sometimes and you feel a lot of pressure at some point of the course, but a hard-working person can surely get through it. Mentors are active and very helpful if you get stuck on something.

2. This question is a big NO for me, here is why: When you are learning something from the very bottom it is super important to learn the hard way, which is the same as the old way. When you come across an easier path, you understand and grasp it way better. For Octave, many tasks require multiple lines of code, whereas in Python it is just one line. You have to do it at least once with Octave to understand how it works in Python.

3. No, you would not probably be able to start a project on your own, you would need some additional source. But, the point is that you now have a general understanding of what machine learning is, what are important algorithms and what are the key points you should consider when doing project. This is the base that every person should have.

DRAWBACKS OF THE COURSE

Although I loved the course, I could not give it 5 stars because it would have been unrealistic. The lectures of the course have an incredible amount of errors. You should be careful. Although all the errors are covered in the Errata section, it still was annoying to open the section every time when I started a new lecture. to check for errors I am about to see.

Another drawback was the programming assignments. They were not explained well and I almost always had to refer to extra Tutorials made by Mentors.

Special Thanks to Professor Ng and all the Mentors!