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返回到 机器学习

學生對 斯坦福大学 提供的 机器学习 的評價和反饋

120,940 個評分
29,696 個審閱


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 16, 2019

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).\n\nAndrew Ng is excellent


Jun 25, 2018

This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.


27901 - 机器学习 的 27925 個評論(共 28,818 個)

創建者 Maxence C

Mar 30, 2018

Great course ! Really interesting and accessible.

I think it can be improved by spending less time on explaining trivial problems with too much example.

創建者 Young-Hun O

Aug 06, 2017

Very basic. Good for beginners.

創建者 Behrouz K

Sep 04, 2017


創建者 nishank

Jun 22, 2016

good course for a new beginners but its also very old compared to modern developments.

創建者 Bhupinder S

Feb 21, 2016

It is very nice course. I appreciate the way, basics of machine learning are taught and

創建者 Itay D

Oct 23, 2017

Liked the course, though I wasn't sure if it's up to date or not.

創建者 Tobias L

Nov 22, 2016

Very good introduction into the practical aspects of machine learning. It's also great that students can work with a free Matlab version for the duration of the course.

創建者 Anirban C

Aug 01, 2017

Very good course to start

創建者 Pallavi J

Mar 04, 2016

Each concept nicely and neatly explained

創建者 Rahul G

Jul 08, 2016

It would be helpful to work on a complex programming assignment at the end of the course

創建者 Chayan C

Apr 04, 2017

I would be a useful if this course teaches about the existing models present in Matlab/octave and not writing rigorous codes every time for analyzing data.

創建者 Nathan B

Jul 05, 2016

Great introductory course to machine learning. There were some quirks to workout in the assignments and the course could use a refresh to make it more current (content is from 2011, I think). However, many of the learnings will build a strong foundation upon which any learner can continue into the interesting discipline of machine and deep learning.

創建者 Ricardo F R

Mar 05, 2017

Great course to learn the math behind the algorithms in machine learning. Not to practical, but definitely helpful to actually understand whats going on when you use a library to help you.

創建者 Julian K

Mar 04, 2017

Extremely good overview of a large variety of Machine Learning Algorithms!

The only thing that bothered me a little was that everything was done in MATLAB instead of R or Python, which is probably more natural for most people taking the course. Also, this course is rather directed at people with a programming background that want to get into stats, not so much for people with a stats background that want to get into the programming part of ML (which I was).

創建者 Srihari S

May 19, 2016

As far as I have learned, I would say that the way the course being taught is simple and understandable.

創建者 Padmakumar N

Aug 17, 2016

Excellent - both content and delivery wise. Thanks very much... More than certifying the course, I believe I've learned immensely from this "not so easy" course, and was able to related quite a lot to some of the work I did on ANNs (MLP) back in the 90s.

創建者 Karthik C

Mar 04, 2018

Excellent Professor! Thank you for the wonderful course. The programming assignments can be challenging for students without a SW background. The ML concepts and implementation techniques are taught well in a structured manner. Thanks - Karthik C

創建者 Daniel E

Jun 02, 2016

Course is well taught and straightforward. Exercises are painfully easy.

創建者 Fuad J

May 09, 2016

A fantastic introductory course for Machine Learning.

創建者 Lucas P

Oct 06, 2016

Programming exercises are really hard and you don't find much help

創建者 José B

Jun 17, 2016

Classes are amazing. Exercises could be a little bit tougher.

創建者 Björn H

Dec 20, 2016

Really nice course, not too difficult. The perfect first step into machine learning! Enjoyed it.

創建者 Julius K

May 28, 2017

Very interesting introduction to Machine Learning. As a maths PhD, some additional mathematical rigour / detail would have been appreciated. Nevertheless a good course!

創建者 Li X

May 09, 2017

nice course

創建者 Adriano B P

Aug 01, 2017

Good introduction for people who want to take the very first step in Machine Learning. Unfortunately, Python is not used.