Chevron Left
Zurück zu Maschinelles Lernen

Kursteilnehmer-Bewertung und -Feedback für Maschinelles Lernen von Stanford University

4.9
114,658 Bewertungen
28,183 Bewertungen

Über den Kurs

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....

Top-Bewertungen

SB

Sep 27, 2018

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

RR

May 19, 2019

This is the best course I have ever taken. Andrew is a very good teacher and he makes even the most difficult things understandable.\n\nA big thank you for spending so many hours creating this course.

Filtern nach:

25826 - 25850 von 27,309 Bewertungen für Maschinelles Lernen

von Mengqi C

Aug 17, 2015

A good introductory course for newcomer in Machine Learning.

von Matthieu V

Oct 20, 2016

Really nice course, but the quizzes kind of take much from the examples presented in the lectures, so it seems to me like learning by heart would be somehow enough to get the grades. Comprehension could be more emphasized in the tests. Said another way, I found the quizzes to be rather easy (some questions were tricky, but many seemed to me rather obvious).

von Ashvin L

Dec 05, 2016

This is a basic course that will provide good fundamentals in ML. If you are already a student with advanced degree in Mathematics/Engg or a professional with decade of experience might find the class a bit slow. However, it was well worth it.

von Dhruvil B

Aug 18, 2015

Great course. Would have liked to have a final project and some more in-depth videos for the mathematics of some of the algorithms. Slides/notes for each lecture or unit or week would be extremely helpful for those who take the class in the future or for those who have finished and would like to review the material. The programming assignments were very interesting and close to the real world. Enjoyed the course overall.

von Padmaja B

Jan 17, 2017

Very informative and a good course

von Vineet R

Dec 03, 2016

A very good experience till now.

von Roman

Feb 22, 2018

That was a very good introduction into main ML topics.

von Blake C

Apr 19, 2016

Good course, needs a little updating though.

von Sharad J

Sep 01, 2017

It's a great course to learn the fundamentals of machine learning. The exercises were very good.

von surya m

Sep 19, 2015

i love machine learning. the course structure is awesome

von Koji

Jan 10, 2016

Great for introduction to this area.

It let us think we understood wide area of machine learning but in fact it's not so simple and not comprehensive.

von Piotr W

Oct 28, 2016

Très intéressant et fait une bonne introduction. Les exercices sont bien trop guidés voir pré-programmé pour les élèves.

von Ashish S

Dec 12, 2015

Just awesome!

von Rishabh J

Feb 22, 2018

Good course for beginners. Need more practical problems in the course so that we can know how to apply in real life problems.

von Malhar K

Jun 18, 2017

The course taught the mathematics behind most of the algorithms and now moving forward, I will not just be using some library function but will have a good understanding of what it does under the hood. Also, the parts of the course that stress on evaluation of the algorithms and areas to focus to improve accuracy, debugging and prioritizing tasks is great. I would have loved to have more programming assignments in the course. Also, the assignments include completing code with the structure already provided. Looking back, if there would have been some I needed to do entirely, I feel it would make me better equipped for further projects in this domain. Thank you!

von Tielman N

Apr 15, 2018

Great class. I thoroughly enjoyed it. I wouldn't have been able to do it without the Lecture Notes. All the best.

von Leonardo C L

Feb 11, 2018

Very good course! After completing all the lessons, videos and exercises, I feel myself much more confident to start machine learning proyects and applications. Although some parts need some update, I cannot but recommend it.

von Trident W

Feb 03, 2016

The course is verg good,and from assignment I also gain a lot.But I still expect teachers can tell us more details.

von Kanishk v

Apr 19, 2018

Good course :)

von sergey z

Mar 11, 2017

Great course, but with many mistakes in the materials.

von Michael O

Jan 24, 2016

All topics are introduced well. The pace of the lectures could be faster.

The programming assignments are a really good addition and help to understand how the techniques work!

von Marco V

Apr 25, 2016

Let me say that this is a great course and that I enjoyed it very much!

My only problem is with the assignment that are, in my opinion, a bit limited and most of the time they focus on liner algebra implementations rather than give a better understanding of the algorithm.

Most of the time the assignments focus on implementing a formula where the algorithm itself is already written for us.

That is good as a first step but in the long run it doesn't give you some practical skills when you want to apply the algorithm to an external dataset.

I would suggest to add some challenges (maybe ungraded) keggle style, like provided this dataset try to get a score better than a value. This will spark some discussion and it would help to apply the concepts and the practical tricks to a real (chewed) problem.

von Vidit S

May 12, 2017

das

von Lingjiao C

Aug 16, 2015

The course contains some fundamental concepts of machine learning. It is not very hard to accomplish the course.

von Nitinkumar B

Aug 24, 2015

Thank you to Andrew Ng for a great ML introductory course for a person with programming background. It gives a very good start for anyone who is clue-less about what "machine learning", "data scientist", "neural networks" etc. really mean. Assignments are designed just right - so that a newbie is not overwhelmed, gets confidence as s/he progresses.

As I mentioned I consider it introductory course, because it gives lot of information which you tend to forget as you move through this course. So you need to practice a lot with real life example before you can become an expert.

Now the challenge is finding a problem that has not been tackled using ML and trying to tackle it with what I have learned.