Chevron Left
Zurück zu Maschinelles Lernen

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

114,367 Bewertungen
28,116 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....



Jul 19, 2019

Amazing course. It gets deep into the content and now I feel I know at least the basics of Machine Learning. This is definitely going to help me on my job! Thanks Andrew and the mentors of the course!


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

Filtern nach:

25776 - 25800 von 27,238 Bewertungen für Maschinelles Lernen

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


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.

von Vladislav Z

Jan 29, 2017

Almost perfect for beginners. There are some glitches in some assignments but I hope that they will be fixed.

von Adam H

Sep 29, 2017

Enjoyed the content and learned a lot. 4 stars instead of 5 because I don't love learning from videos and would like something where I can control the pace a bit (both faster and slower).

von yettella s p r

Jun 18, 2018

It gives a boost and motivates you to learn machine learning. Also some complicated topics are not explained in detail due to the time constraint. But this is definitely a kick start for your future of AI and machine learning

von Fei P

Oct 08, 2015

Great course! A little on the easy side for people who have seen ML before, but definitely offering many great insights and tools with which one can improve upon their models.

von Naman J V

Nov 28, 2017

An amazing course to start off with. Thank you Andrew for this wonderful course

von Harini D

Aug 31, 2016

Great Course for beginners!

von harsh T

Dec 04, 2017

A perfect start in the growing world of machine learning. Covers all the neccessary topics, with taking due care of the conventions and industry requirements.

von Ido N

Oct 23, 2017

Great introduction to machine learning, and it gets you through all the concepts, ideas and terms nicely. I feel like this course is perfect for people who manage tech teams and people who wants to take their first step into the subject. Having said that, after completing the course I don't feel like I can go and build my machine learning system and that I should learn another libraries and toolkits in order to apply what I learned in real world programming.

von Miguel C R

Dec 16, 2015


von Francis

Oct 01, 2015

Quite informative and the Tutor is good

von Morin D

Jan 27, 2017

The course is really great to understand machine learning from scratch! Videos are really instructive and the many quizzes and exercices are well thought. The only (small) inconvenient is that sometimes the learner spend a lot of times explaining some basic concepts making the video a little long. Still, I highly recommend this class to anyone who wants to learn about machine learning!

von Chinmay J

Jun 29, 2016

Great course!

von Robert V

Feb 08, 2016

Great introduction to Machine Learning