Chevron Left
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

4.9
121,268 個評分
29,773 個審閱

課程概述

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....

熱門審閱

HS

Mar 03, 2018

My first and the most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course. Thanks Andrew Ng and Coursera for this amazing course.

ML

Aug 19, 2017

Very helpful and easy to learn. The quiz and programming assignments are well designed and very useful. Thank Prof. Andrew Ng and coursera and the ones who share their problems and ideas in the forum.

篩選依據:

28001 - 机器学习 的 28025 個評論(共 28,898 個)

創建者 Hisham S

Dec 07, 2016

Very good course overall, but it does need some cleaning up. Lots of small typos and errata have been documented in both the lectures and the homework assignments. There seems to often be inconsistent row/column formatting of X and/or theta in the homeworks versus the lecture notes which creates unnecessary confusion. Although it is possible for the student to browse around multiple areas to piece together the puzzle, given that the course has been around for several years I feel that there should be a cleanup and reconsolidation of things to save students time. But yeah... given that you can take this course for free... it's pretty badass :)

創建者 Miguel S M

Dec 14, 2016

It is a good course with clear explanation. I recommend it.

創建者 Benjamin H

Feb 28, 2017

Great course. Learned a lot.

Would have liked more practical exercises rather than pre-made scripts that we edit.

創建者 Choon C N

Feb 01, 2018

Great course and teacher! It improves my knowledge of machine learning. Looking forward to learn deep learning.

創建者 Tiago T

Aug 31, 2017

Some quiz questions are a bit confusing in the sense that they ask for "approximate values" and the answers are quite round values which can confuse a bit on your answer (last quiz particularly)

創建者 Bernhard S

Oct 29, 2015

Andrew Ng walks you through some complex material in a way that makes it easy to grasp the basic notions. The assignments are very well documented and the code skeleton that's provided makes it easy to complete them (maybe a bit too easy - someone would have a rough awaking when having to code all this from scratch again as they apply it to their real jobs).

創建者 Жаворонков С В

Oct 14, 2017

There are many mistakes in videos

創建者 Daniel W

Mar 23, 2017

Very well explained. I was able to follow easily. The basics are there. The tutorials were very valuable. Would be nice if the cours was updated by more modern topics like deep learning.

創建者 Igor T

Jan 24, 2016

The course gives a nice overview of ML. I only wish it was in python.

創建者 Akshay C

Oct 05, 2017

Only drawback is they make you code in MATLAB/Octave.

創建者 Camillo G

Mar 05, 2017

Great Course! It covers main concepts of ML with a good explanation. In my opinion, the PCA part could be improved because I used other sources to understand well some concepts

創建者 Jaydeep

Jun 24, 2017

Very Great course for beginners by the legend of machine learning

創建者 Kevin G

Jul 07, 2017

All concepts are explained well,gives a great insight for a beginner into machine learning

創建者 Rahul S

Dec 20, 2016

Nice teaching

創建者 Deleted A

Apr 29, 2017

With excellent and detailled explanations.

創建者 Déodat V

May 26, 2018

Really good intro to machine learning algorithms

創建者 Iskya G

Jun 20, 2017

Great course, it will be great to add some references and extra/optional excercise, examples and/or tutorial

創建者 guillaumeviland

Jun 19, 2017

Receiving the course from Andrew is a great pleasure, he is really involved in giving the best transmission to students. And a particular thanks for the quality of exercises.

創建者 Stijn C

Jul 27, 2017

Neural networks is still very unclear but for the rest all information provided is great. The course provides many insights on the subject for people with little prior knowledge.

創建者 QianChen

Jun 28, 2017

Thank you for helping me to understand more about ML, and may be in some topic like neural network need more material to deep understand and use in real life.

創建者 Chaitanya B

Aug 04, 2017

Great course. Happens to be my first exposure to knowledge machine learning (never read any book or tutorial prior to this). I loved several aspects of the course, such as: The logical sequencing and building up of concepts, the use of problem-case to introduce each algorithm, the frequent switching over between math and real-world, the constant attention to tiny formulaic detail and rigor while still maintaining an easy-flowing conversation. I also perceived some areas of improvement (which I mention hesitatingly, as I'm very new to the field): (1) Should do more to sharpen student's skill in tying a high-level real-world problem description to a catalogged ML problem (2) Need to provide help in understanding why the student's answer on a Quiz question was wrong. (3) I felt there was some crucial gap in the course material on NN that hit me hard while doing the programming task for back-propagation (4) A basic intro to NLP would have been great for someone like me who is at zero on the subject. (5) I was left wondering what the term "Deep Learning" meant, and where it fits, vis-a-vis ML. (6) For each of ML algorithm introduced, it would have been great if it ended with a clear statement of what concepts would be already implemented in off-the-shelf libraries, and what concept would remain to be judiciously applied / implemented by us.

創建者 Hamed M

Jul 08, 2017

This is an interactive course for understanding Machine Learning from scratch. I definitely recommend this course.

創建者 Alu

Jun 18, 2017

Phenomenal course, I found myself transported back to my college studies which I enjoyed very much. Would have been cool if there was a video introducing topics not talked about in this course such as RNNs and such. Thank you Sir!

創建者 Aswin S

Aug 09, 2017

Excellent

創建者 Toke Z

Nov 05, 2017

A really excellent course for practical implementation of various algorithm. This will suit users who are not seeking a deep mathematical understanding of these topics, even thought Andrew tries to show the general idea for a lot of the concepts. Overall a great course, and good coding practice! :-)