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

114,559 Bewertungen
28,160 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....



Jun 15, 2016

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.


Nov 11, 2017

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.

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25851 - 25875 von 27,286 Bewertungen für Maschinelles Lernen

von BRUK T

Oct 19, 2015

It is very interesting but the data collection and preparations in every example is not clear. please work on it.

von Jean-Pierre B

Aug 20, 2015

Great course for understand the basics and some of the tricks

von Jack M

Sep 28, 2015

I would give 4.5 stars but not full marks because I don't believe there is sufficient support for novice programmers. More worked examples earlier on with MatLab would have been invaluable and the first week could have an easy programming project to establish the basics via practical experience rather than lecture.

However, I found the material to be otherwise fascinating and inspiring in the potential power of Machine Learning to apply mathematical principles to ameliorate the human condition. This has confirmed the path I have chosen. Thank you.

von Arijit D

Apr 23, 2017

The course material is great and its an awesome starting point for data analysis. However, its very unfortunate that the assignments are in Matlab / Octave. It's very important to study Python for interviews, as that is the most popular language with excellent data science tools. Also, the Octave environment is not the most stable and I often encountered problems. I wish Professor Andrew Ng had taken industry needs and data science needs into consideration while selecting the platform.

von 林己

Feb 17, 2017

Good for beginner, but somehow too simple for further study.

von Paresh A

May 15, 2018


von Paveen J

Jul 08, 2018

Excellent materials and real-world examples.

von Pat K

Jan 22, 2016

Great mix of low level technical details, exposure to a variety of topics, and high level strategies.

von Paul C

Nov 24, 2015

The programming exercises were too structured and the course was quite easy.

von Wesley S

Nov 08, 2015

Good introduction to machine learning although the later exercises were quite easy. Once you get through the neural net back propagation section, there really isn't much of a challenge left. That said, you do get a good sense of how the various algorithms are implemented and can self-study from there.

von Mitul T

Jun 22, 2017


von Sidhardh M

Jun 26, 2017

more examples can be provided

von Hanzhang W

Dec 05, 2016

The content is somewhat easy. Adding more in-depth analysis and concepts would be really helpful.

von Preethi D V

Aug 05, 2017

Great course to get started on machine learning. However, its best if we could come to this course after brushing up on some basics of linear algebra and statistics.

von Misha D C

Dec 28, 2015

This course is informative. It is structured & executed smoothly.

von Shakir A

Jan 03, 2017

very nice course which explains the basic theoretical concepts and implementation ideas

von Aashik J

Sep 05, 2017

It's a bit outdated now, I think. Plus, his lectures that are available in YouTube are way more challenging/fascinating. Looking forward to his latest AI course though.

Nothing can take away the fact that he is an amazing teacher.

von nobita

Apr 26, 2016

A great, succinct course is for begin learner.

von HanByul Y

Apr 17, 2016

This course is very kind and easy to learn.

von Petr L

Aug 06, 2017

Solid foundational knowledge for those starting in ML. I like the structure of the class overall, building up on content of previous section, and the choice of MatLab as the the language/programming environment. I'd wish there was more time spent on mathematical aspects (e.g. for PCA or backprop), but I do understand that's probably unrealistic for an online class, and the additional theory could be gained elsewhere. Good practical advice on optimizing and tuning the ML systems (e.g. week 6), though appreciating this knowledge probably takes more practical applications. Finally, I found the quiz sections in Week10/Week11 to be more cursory compared to the other weeks, and wish there were practical assignments in the last two sections.

von Ricardo P

Apr 25, 2018

I like the way Andrew explains and highlight important details for the real world.

von Abhimanyu C

May 04, 2017

Course only gives an overview.

von Ivan R P

Sep 29, 2015

Wonderful course! Sound quality requires revision, but content is supreme.

von Joe K

Feb 06, 2016

Great! This is a great course to learn the idea of machine learning even I am not a computer science and data science student. It provides a chance for us to study different type of machine learning algorithm. The assignment also gives you a chance to practice these algorithms on some application example. If this course can provide more deeply mathematics algorithm or guideline for future study on learning algorithm, it will be a perfect course!

von Stephen M

Oct 04, 2015

Video lessons are excellent. Homework is well-written and does a good job of illustrating the lessons.