Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai. He has successfully spearheaded many efforts to "democratize deep learning" teaching over 2.5 million students through his online courses.
Therefore about 50000 students have taken the course. By looking at the change in thread over the first and last week , about 5–15% of the students actually finish the course. Many students may finish the course without participating in any group discussion and many post multiple threads thus the information isn't adequate for a specific answer.
Jun 07, 2018 · In fact, Ng’s original Stanford MOOC remains the most popular course offered by Coursera. Since the course began in 2012, it has drawn more than 1.7 million enrollments. (It now runs on demand, so people can sign up anytime.) And his new series of courses through Deeplearning.ai, which kicked off last year, have already exceeded 250,000 signups.
Nov 13, 2019 · Stanford’s Machine Learning course taught by Andrew Ng was released in 2011. 8 years after publication, Andrew Ng’s course is still ranked as one of the top machine learning courses. This has become a staple course of Coursera and, to be honest, in machine learning.. As of this article, it has had 2,632,122 users enroll in the course. That is just enrolled in, but …
He has successfully spearheaded many efforts to "democratize deep learning" teaching over 2.5 million students through his online courses. He is one of the world's most famous and influential computer scientists being named one of Time magazine's 100 Most Influential People in 2012, and Fast Company 's Most Creative People in 2014.
Andrew Ng | |
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Fields | Artificial intelligence, machine learning, natural language processing, computer vision |
Andrew Yan-Tak Ng ( Chinese: 吳恩達; born 1976) is a British-born American computer scientist and technology entrepreneur focus ing on machine learning and AI. Ng was a co-founder and head of Google Brain and was the former Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people.
In 1998 Ng earned his master's degree from the Massachusetts Institute of Technology in Cambridge, Massachusetts. At MIT he built the first publicly available, automatically indexed web-search engine for research papers on the web (it was a precursor to CiteSeer / ResearchIndex, but specialized in machine learning).
Ng was a co-founder and head of Google Brain and was the former Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people. Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its Stanford AI Lab or SAIL ).
Ng was born in United Kingdom in 1976. His parents are both immigrants from Hong Kong. Growing up, he spent time in Hong Kong and Singapore and later graduated from Raffles Institution in Singapore in 1992.
Research. Ng researches primarily in machine learning, deep learning, machine perception, computer vision, and natural language processing; and is one of the world's most famous and influential computer scientists.
In 2017, Ng said he supported basic income to allow the unemployed to study AI so that they can re-enter the workforce. He has stated that he enjoyed Erik Brynjolfsson and Andrew McAfee's " The Second Machine Age " which discusses issues such as AI displacement of jobs.
As of 2020, three of most popular courses on Coursera are Ng's: Machine Learning (#1), AI for Everyone, (#5) , Neural Networks and Deep Learning (#6). In 2008 his group at Stanford was one of the first in the US to start advocating the use of GPUs in deep learning.
Andrew Ng is the co-founder of Google Brain and Coursera, and an adjunct professor at Stanford University. He was also a former vice president and chief scientist at Baidu working on large scale artificial intelligence projects. Therefore, without a doubt, Andrew Ng is one of the most knowledgeable people in the world for teaching machine learning.
K-Means Clustering is one of them. It is used to group data into different clusters by procedurally moving centroids towards the gravity of the data points.
Support Vector Machine (SVM) is another solution to classification problems. The instructor does not go in depth into this subject, but still gives us a clear view on its mechanics and its intuition. The overall idea is to compute new feature depending on proximity to established landmarks.
Principal Component Analysis allows us to approximate and transform high-dimensional feature sets into lower dimensional ones.
The basic syntax of Octave programming language is introduced here. Since Octave is practically a carbon copy of Matlab, so people with good knowledge in Matlab could skip this section. However, those who are not familiar with scientific programming languages like Matlab would find this section useful because you will learn how to manipulate matrices in programming languages as well as vectorizing a problem.