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Apprentissage automatique, Université de Stanford

4.9
90,650 notes
23,114 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 MM

Oct 08, 2017

This course was my first contact with ML and it was a good surprise.\n\nThe classes were very clear and it was very useful for me.\n\nI strongly recommend for those who want to learn the basics of ML.

par DW

Feb 20, 2016

Fantastic intro to the fundamentals of machine learning. If you want to take your understanding of machine learning concepts beyond "model.fit(X, Y), model.predict(X)" then this is the course for you.

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22,266 avis

par Mr_again

Jan 18, 2019

actually a good course

par Roei Ben-Harush

Jan 18, 2019

10/10. Andrew is an amazing teacher. Thanks!

par Praveen Kadala

Jan 18, 2019

This course is very well put together from beginning to the end. The simplicity and approach towards teaching this kind of new and complex subject is amazing. I highly recommend for anyone who would like to get into Machine Learning space.

par Vana Čolović

Jan 18, 2019

I enjoyed very much this class. Thank you Andrew Ng & Coursera.

par Pramit Adhikari

Jan 18, 2019

I really like the way how learning made easy.

par John Dobson

Jan 17, 2019

Amazing course, very good customer service. Highly reccomend!

par veronica yu

Jan 17, 2019

Great class for introduction in machine learning! Very easy to understand and follow through.

par Nitish Bhardwaj

Jan 17, 2019

It's been a great time to learn Machine Learning through this course. Course contents are very good as well as Prof. Ng.

Just wanna say "Thank you so much" Prof. Ng. :)

par jorge linero

Jan 17, 2019

Excellent course. Presentation by Andrew Ng is very, very good. He is really an excellent teacher. The material was, literally, amazing. Particularly the neural networks and SVMs. The gradual introduction of complexity is very well thought out. Practical guidance for successful implementation is very valuable. Lastly the supporting material is great, including lesson slides, lecture notes, and particularly the text and code supporting the exercises.

par Hüseyin Onur Yağar

Jan 17, 2019

Amazing course. Prof. Andrew Ng is the best in the are of AI. He can teach you what you need to know in machine learning area.