Andrew Ng's DeepLearning.AI, in partnership with Stanford Online, recently announced a new Machine Learning Specialisation course on Coursera. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The 3-course program is a new version of Ng's pioneering machine learning course, taken by over 4.8 million learners since 2012. The program provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation. The new Machine Learning Specialization by @DeepLearningAI_ & @StanfordOnline is now available on @Coursera!

DeepLearning.AI's Andrew Ng has announced the winners for the Data-Centric AI competition. Under the category of Most Innovative, the winners included Mohammad Motamedi, Johnson Kuan and the GoDataDriven group Part of Xebia. Andrew Ng also gave honourable mentions to Pierre-Louis Bescond and KAIST-AIPRLab (Youngjune Lee, Oh Joon Kwon, Haeju Lee, Joonyoung Kim, Kangwook Lee). As only less than 10K images were allowed, participants had to focus on getting "Good Data" in the absence of "Big Data". Andrew NG feels that this phenomenon is very common in AI applications for more traditional industries.

DeepLearning.AI's Andrew Ng recently announced the launch of the NeurIPS Data-Centric AI workshop. The workshop is expected to showcase some of the best academic research work related to data-centric AI. Academic researchers and practitioners can submit their research papers on or before September 30, 2021. This will be the premier event for academic research on Data-Centric AI. Deadline for research article submissions is Sep 30th.

Even though I did not follow his older courses, they seem really appreciated, at least on this subreddit. I hope these new ones will set an even higher standard. That way, newcomers may share an identical set of notations, principles and methodologies so we can all focus on other tasks, such as visualization. You will practice all these ideas in Python and in TensorFlow. What do you guys think of this choice?