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Apprentissage automatique, Université de Stanford

4.9
102,166 notes
25,391 avis

À propos de ce 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

par SS

May 17, 2019

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

par NN

Oct 15, 2016

It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.

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24,533 avis

par Abhishek Shrivastva

May 20, 2019

it's an awesome course.

par Andrew Dettor

May 20, 2019

this shit is fuckin lit bruv

par Mujeeb Ur Rehman Shariq

May 20, 2019

one of the best Course available. Gives a detailed basic knowledge from one of the best Machine Learning Expert. I Highly recommend taking it before going into Deep Learning.

par majutharan ananthamohan

May 20, 2019

Very good teaching method. I'm very happy to learn this course. :)

par Daniel J Spinosa

May 20, 2019

Great intro to ML to build a solid foundation. Seems a bit dated, but that just means you can't stop here.

par David Li

May 20, 2019

I've learned lots of skills of machine learning, such as linear regression, logistic regression, neural network, K-means, PCA, recommend system, photo OCR and so on.

I wanna thank Andrew Ng for his fabulous course, because of its perfect videos, lectures, quizzes, and programming assignments. I am also great honored to be taught by him, even it was only through the network.

par Maya Saghiv (Tadmor) - Israel!

May 20, 2019

An excellent course for Machine Learning Introduction!

Its content is extremely important and useful for being an expert in the field.

It will be helpful to also have "readings" for the more advanced weeks .

The Programming Assignment are too difficult in my opinion for an introduction course.

par dongshandake

May 20, 2019

语速太快

par Kumaresan G

May 20, 2019

As a beginner in ML i found this course to be very useful. Andrew introduced a variety of topics with proper balance between fundamentals and the math derivation parts. This course has given me tremendous confidence to tackle variety of problems using ML. Andrew is an excellent teacher. I would highly recommend this course to anyone who wants to get started on ML. Thanks.

par Григорий Пискун

May 20, 2019

Andrew Ng explains the information in a very clear manner. The course is nicely structured, contains many informative examples and graphics. Programming tasks for Octave are prepared, data gets loaded automatically. Students are focused on the actual algorithm implementation tasks rather then on the boilerplate code. Also I found task submission feature very useful because it provides a convenient approach to get task uploaded and assessed almost instantly.

Thank you very much for a such a great course!