Chevron Left
Back to Machine Learning

Machine Learning, Stanford University

4.9
85,642 ratings
22,015 reviews

About this Course

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....

Top reviews

By JP

Oct 25, 2016

Great course. A progressive discovery of the maths inner to the learning algorithms. This course gives that insight many ML practitioners don't have and is so important for making real use cases work.

By SB

Sep 27, 2018

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

Filter by:

21,161 Reviews

By Santiago Ibañez Caturla

Nov 12, 2018

El mejor curso para iniciarse en Machine Learning. No es para aplicarlo directamente pero te sirve para sentar las bases para aprender posteriormente de forma práctica con otros cursos, como el de Deep Learning del propio Andrew Ng o el de fastai.

En definitiva, muy recomendable

By Derek Lam

Nov 12, 2018

Very good. Thanks. I have learned a lot in the course.

By Nicolas Remerscheid

Nov 12, 2018

Excellent teaching and course, interesting and very comprehensible! Thank you Mr. Ng!!

By Diya Bandyopadhyay

Nov 12, 2018

Best course for ML beginners. It's intriguing and understandable.

By Akinori Emoto

Nov 12, 2018

とてもいいコースでした。

私は機械学習について何も知らない全くの初心者でしたが、機械学習の全体像や考え方をこのコースから学びことができました。

数学のように内容が厳密でなく、少し理解の深さに不満な箇所があったり、またOctaveの使い方に戸惑ってプログラミング課題に思いの外時間を取られたりもしましたが、仕事をしながら時間を作って頑張る価値は本当にあると思います。

By utsab Bhattacharjee

Nov 12, 2018

very nicely explained and very well structured course

By 刘欣

Nov 12, 2018

讲得精辟、准确。编程作业设计的特别好,通过实践,对讲授的内容理解更深刻,方便以后灵活运用。

By Yash Modi

Nov 12, 2018

This course provide great fundamental knowledge of machine learning. Why are we applying machine learning? How to apply it? What are the efficient way of applying? what are the benefits? These all questions are answered by this course. Also learned gnu octave for implementations.

By 木一

Nov 12, 2018

很棒的课程,学会了很多东西,感谢Andrew Ng!

By Duc-Vinh Vo

Nov 12, 2018

The complicated algorithms were explained in a way that cannot be easier to make the learners be profound. Typical algorithms such as supervised and unsupervised learning algorithms, support vector machine, PCS, Clustering, Anomaly Detection, and so on were taught and explained completely. Furthermore, many practical problems in the field also considered during the course assignment like logistic regression, anomaly detection, spam-email classification, image compression,.. to help the learner sharpen their skills of not only modeling practical problem into computer ones, but also the programming skills.