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Avis et commentaires pour l'étudiant pour Apprentissage automatique par Université de Stanford

4.9
121,268 notes
29,773 avis

À propos du cours

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....

Meilleurs avis

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.

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176 - 200 sur 28,892 Examens pour Apprentissage automatique

par Larry C

Feb 24, 2016

There are too many mistakes and misleading statements made in the course material. There were a lot difficulties with submitting assignments in order to move forward in the course. I had to give up because I don't have time to be bogged down like this.

The students' comments and discussion would be useful if they can be accessed from within each lesson. I can't make heads or tails of what the discussions were referring to, when they are all clumped together at the course web site instead.

par Andy M

Sep 08, 2018

Huge amounts of assumed understanding make this course impenetrable.

par Subham B

Aug 30, 2019

This course is definitely not for beginners.

par David C

Apr 02, 2019

Have to give a star so I will give it one. Others rate this course highly. I don't know why.

Course states no requirement for knowledge of linear algebra. However this is not really practical and seems disingenuous. I have spent a lot of time re-learning linear algebra.

I have spent much more time on the work than the course states and unless you are currently involved in similar work you probably will too.

I have never received any response to the feedback I provided.

Many times I have been frustrated because the math material is treated casually but then later success on quizzes and assignments are based how well you understand the math. So while the instructor and content can treat the math as casually as they wish, unfortunately, you cannot be so casual.

par Bayram K

Feb 17, 2017

I would rename this course as Programming Octave with Application to Machine Learning rather that Machine Learning. Once you start the course you will have to focus on Octave rather than on ML topics if you want to do programming exercises. There is no degree of freedom in programming. You are provided with a lot of weird Octave codes which you will have to complete instead of writing yourself from scratch. More than 50% of my time was spent in order to learn Octave and understand (guess!!!!) Octave codes.

So, if you really want to learn ML and try it in practice this course is not for you. However, you could just watch the videos whose level is not more that elementary introduction to ML.

par omri g

Nov 11, 2015

Been asked to re-take all assignments *after* paying for a certificate! I wil never pay for a Coursera course again, and I would not recommend my friends to do so

par 黄昕宇

Jan 27, 2019

十分受益,讲的非常棒

par Wilson D L R

Jan 28, 2019

I'm loving it !

par Jean H

Jan 28, 2019

What a great course

par Siddish R

Jan 29, 2019

The best course on Machine Learning in entire internet. Period

par Mohammad R E

Jan 29, 2019

I found this course amazing and learned so much from it. The lectures were smartly arranged, followed by the well-designed assignments. While working on the assignments, I felt that the guidelines are leading me step-by-step to implement what I have learned and find the solution. I am amazed by the huge effort of people who brought this brilliant material to the world. Every thing was perfect, thank you all!

par Ahmet

Jan 29, 2019

This course covers the basics of ML. The lectures are brief and educative, yet teaches the core with examples. After three years passed, I do still watch videos over and over again.

par Heena N

Jan 29, 2019

Great course.

par Maxim Y

Jan 28, 2019

It is really nice introduction to machine learning. With good math explanations. Andrew Ng is really great teacher and expert in that field. So, thank you a lot for your course! Good luck!

par Abhishek Y

Jan 29, 2019

The course is well structured and it was really enjoying to go through this course. Thanks Prof Andrew Ng.

par Tomer B

Jan 29, 2019

Amazing course that is well presented. Andrew make an EXCELLENT job teaching that class. It is an amazing introduction to machine learning, and it's applications today. Highly recommended

par ANUP K

Jan 29, 2019

The best ML course. And Mr Andrew Ng is the best instructor in the world.

par Pran S

Jan 29, 2019

Fantastic learning environment. and no prerequisites, just little math and sharp brain to hold the ground.

par Bhaumik K

Jan 28, 2019

Very Insightful

par David A F

Jan 28, 2019

Amazing Course. With a bit of effort even someone with very little notions of programming can follow the course and complete the exercises. Really enjoyed this journey through Machine Learning with the amazing Andrew Ng. Thank you so much for this course.

par Hoang K K

Jan 28, 2019

Amazing course!

par Ruslan P

Jan 28, 2019

nice course

par 薛遥 X Y

Jan 29, 2019

I personally became a fan of Andrew Ng. Hopefully I may have the change to work with him someday in the future

par Marcel D S

Jan 29, 2019

Absolutely fantastic course. Professor Ng's approach to teaching is a bit more top-down, so you'll get a lot of useful theory and intuition about how the different Machien Learning algorithms presented in this course actually work. Do not expect to be terribly fluent in implementing these algorithms after completing this course, though. I found that the programming exercises really show their age and although I recognize that Matlab/Octave is a nice programming language to implement matrix operations, I still feel that having to use this language is the weakest part of this course. But there is no single course that will teach you everything you need to know about Machine Learning. Considering the huge amount of intuition I've gained, I feel like this "11 week" long journey (it certainly took me longer than that!) was one of the most elevating educational experiences I've had to date.

par Suraj G

Jan 27, 2019

Great learning!