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Machine Learning, Stanford University

(102,345 ratings)

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


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


Nov 11, 2017

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

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24,575 Reviews

By 王浩宇

May 23, 2019

Nicely! Very useful and understandable for a pure beginner. No requirement for knowledge on coding, and just a little bit of that on calculus.

By Yaremchenko Sergiy

May 23, 2019

Thank you, Andrew Ng!

By Aakash Singh

May 23, 2019

Greatly helpful if you want to know all the basics of Machine Learning. Professor Andrew Ng makes you understand every little thing and the assignments are greatly helpful. It was my first course on coursera.

By Edwin Acuña Montero

May 23, 2019

Muy bueno, el material y los ejercicios son muy formativos y el profesor explica muy bien


May 22, 2019

Great course, brought me into a new world!! Thank you!

By Refqa Gerges

May 22, 2019

كورس مفيد

By Inbar L

May 22, 2019

It was very informative and interesting, and I feel it has really helped me understand the concepts of machine learning.

By Mateusz Klinowski

May 22, 2019

Great course, it put my math, statistics and analytical skill in the test. I am very thankful to Prof. Andrew Ng for creating such a greate source of knowledge about Machine Learning.

By Animesh Sinha

May 22, 2019

Great course, but now seeing the deep learning specialisation seems a little out of date, unnecessarily using a worse notation with theta. Adds a little to the common stuff, but does not isolate it's portions well. Should have had a completely different course for Neural Nets and one for SVMs, etc. Get's boring in the last 2 weeks where there are no assignments. Still very useful.

By Syed wasim nihal

May 22, 2019

Great course for beginners.