|Machine Learning At All Levels On Coursera|
|Written by Sue Gee|
|Thursday, 29 November 2018|
Andrew Ng's inaugural presentation of an introductory course on machine learning will begin on Coursera early in 2019. Meanwhile the courses he teaches at intermediate to advanced level have just re-started for their final presentation for 2018.
In the context of AI and Machine Learning (ML), Andrew Ng needs no introduction. The co-founder of Coursera, he was the founding lead of Google’s Brain Project, and served as Chief Scientist at Baidu before embarking on two artificial intelligence startups - Deeplearning.ai, which is a training company founded in 2017, and Landing.ai, also founded in 2017, which has the aim of transforming enterprises with AI. At the same time he is an adjunct professor at Stanford University.
He has also been working on a book "Machine Learning Yearning" which is intended to teach you how to structure MLprojects, making draft chapters available for free to anyone who signs up to a mailing list.
Free learning is something Andrew Ng has long been associated with. His Stanford University Machine Learning lectures were put on You Tube in 2008 and over 200,000 people had viewed them there prior to the launch in Fall 2011 of the MOOC version for which the material was organized into short segments with quizzes and hands-on programming assignments to consolidate learning. Although the initial presentation of the Machine Learning MOOC pre-dated the launch of Coursera it undoubtedly was one of the triggers for the formation of the platform.
Machine Learning still runs on Coursera where it has a popularity rating of 4.9 (out of 5) based on more than 86,700 reviews. It also has the distinction of being #2 in the league table of the The 50 Most Popular MOOCs of All Time in terms of numbers of students, with total enrollment having exceeded a million students by 2016.
Machine Learning, which restarted on November 26th, is currently presented as an 11-week course (55 hours in total). At intermediate level, it provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
Using numerous case studies and applications, it shows 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.
Completion of this course is considered a good pre-requisite for tacking Coursera's Deep Learning Specialization, created by Andrew Ng's company Deeplearning.ai and taught by Ng with teaching assistants. It comprises five courses, between 2 and 4 weeks each (77 hours in total), and requires enrollment in a monthly subscription plan that gives you access to Coursera's entire catalog. You can audit the course material for free by enrolling in each one separately but without access to its graded assignments,
According to the blurb
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
The newly announced course, AI For Everyone, also comes from Deeplearning.ai and will be taught by Ng. It is described a non-technical course that anyone can enroll in to expand their general AI knowledge. It is part of Ng's bigger vision to democratize access to this knowledge and lay a foundation that enables AI innovation to be distributed to businesses and individuals globally, in a fair and equitable way.
In particular the 3-week course, less than 10 hours in total, is aimed at business leaders with the idea of helping them understand technologies like machine learning and deep learning to be able to spot opportunities to apply AI to problems in their own organizations. The course also provides guidance on how to manage and organize AI teams within companies more effectively as well as the basic tools to understand AI-oriented production and projects and, in turn, help learners cut through the hype to discern what AI truly can and cannot do.
According to the course blurb:
If you are a machine learning engineer or data scientist, this is the course to ask your manager, VP or CEO to take if you want them to understand what you can (and cannot!) do.
Enrollment is already open and its inaugural presentation will take place early in 2019.
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|Last Updated ( Friday, 30 November 2018 )|