Andrew Ng’s Machine Learning course from DeepLearning.AI on Coursera has been revamped and updated and its student ratings suggest it is better than ever. It now uses Python and introduces TensorFlow, but it still covers all the basics.
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Not many online courses qualify as “MOOCs” these days but I can make an exception for the three courses that go to make up the Machine Learning Specialization offered by DeepLearning.AI and Stanford on the Coursera platform. If you want to earn a certificate at the end, and enjoy the full experiences including quizzes and assignments you will need to enroll and pay a monthly subscription (financial aid is available) but if you just want to view the course materials you can audit the course for free. The M in MOOC is for massive and after only a fortnight over 20 thousand students are enrolled
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.
Andrew Ng’s course on Machine Learning was one of the very first courses from Coursera when it first launched in 2012. Machine Learning was a new concept at the time and it was understood as a new label for content that could equally well be described as applied statistics. The 2012 course was already an updated version. Andrew’s Ng had put his Stanford University lectures on You Tube in 2008 and these had had over 200,000 views by the time they were converted to online format in Fall 2011 and were offered for free, attracting a record breaking 104,000 students, 13,000 of whom gained certificates. As a graduate of the 2012 version of Machine Learning I have had no hesitation in recommending it and the other courses that have derived from it.
So what’s different in this version?
It now uses Python rather than Octave, which will give it a broader appeal given Python’s popularity. There’s an expanded list of topics that encompass and updated list of machine learning concepts, including modern deep learning algorithms, and decision trees, and tools such as TensorFlow.
There are also new ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing and make it easier to complete programming exercises and a practical advice section on applying machine learning, which draws on emerging best practices from the last decade.
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