Machine Learning MOOC Updated

Python book
Photo by Christina Morillo

Andrew Ng's Machine Learning course on Coursera has been revamped and updated and it is getting good student ratings.

There are fewer online courses that I consider to be true MOOCs now. Massive is small. Open is more closed. But the "OC" portion remains for many. The three courses that make up the Machine Learning Specialization offered by DeepLearning.AI and Stanford on the Coursera platform still fit the MOOC definition more closely.

You can earn a certificate at the end, and enjoy the full experiences including quizzes and assignments if you enroll and pay a monthly subscription but the courses are free (Open) to audit and view the course materials. The Massive in this course is massive with over 20,000 students enrolled.

Andrew Ng is the co-founder of Coursera and was the founding lead of Google’s Brain Project, and served as Chief Scientist at Baidu. He then did two artificial intelligence startups - (a training company founded in 2017) and (for transforming enterprises with AI). He remains an adjunct professor at Stanford University. His course on Machine Learning was one of the very first courses from Coursera when it first launched in 2012. I audited the course that year though I knew the content was way above my abilitiees but I was curious as to the structure of the course from a design perspective.

At that time, Machine Learning was a new concept and was close to applied statistics. Ng goes way back because his Stanford lectures were on YouTube in 2008 and got 200,000 views. Then, he converted them to an online format in Fall 2011 and they were offered for free. He had 104,000 students and 13,000 of them gained certificates.

On the tech side, this updated version:
uses Python rather than Octave
expanded list of topics including modern deep learning algorithms, decision trees, and tools such as TensorFlow
new ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing
programming exercises
practical advice section on applying machine learning based on best practices from the last decade


Teaching Artificial Intelligence in K-12 Classrooms

Should K-12 students be learning about artificial intelligence? Since the turn of the century, I have written about, observed and taught in programs to have all students learn the basics of coding. Prior to that, robotics made big moves into K-12 classrooms. AI seems to be the next step.

I saw recently that launched a day for classrooms around the world to participate in learning about AI. They offered resources from MIT for teachers, including lesson plans and videos for all grade levels.

car gps
New vehicles have many AI-assisted applications Image: Foundry Co

It's not that students aren't already surrounded by artificial intelligence in their everyday lives, but they are probably unaware of its presence. That is no surprise since most of the adults around them are equally unaware of AI around them.

You find AI used in maps and navigation, facial recognition, text editors and autocorrect, search and recommendation algorithms, chatbots, and in social media apps. If you have a smartphone to a new car, you are using AI consciously or unconsciously. Consciously is preferred and a reason to educate about AI.

Though I have never thought of my time as a K-12 teacher as training students for jobs in the way that teaching in higher education clearly has that in mind, you can't ignore what students at lower level might need one day to prepare for job training in or out of higher ed. Artificial intelligence, data analytics, cloud computing, and cybersecurity are areas that always show up in reports about jobs now and in the near future.ed workers which means that we need to do more to prepare our students for these careers and others that will evolve over time.

“AI will dominate the workplace and to be successful, people are going to have to understand it,” said Mark Cuban, who launched a foundation in 2019 that provides AI bootcamps for free to students to learn about AI. It is his belief and the belief of other tech leaders and educators that artificial intelligence is something that should and can be taught at all levels, regardless of a teacher’s experience in this field.

One starting place might be Google AI Experiments which offers simple experiments to explore machine learning, through things like pictures, drawings, language, and music. See

AIClub offers courses for students and free resources for educators including professional development sessions to spark curiosity for learning about AI. They are also developing guidelines for AI curriculum in grades K through 12.

I tried an AI test (it is rather long for younger students) at that was part of a survey for a research study about AI-generated content. It shows you images, texts, and plays sounds and asks you to decide if you think they show real people or were created by humans or not. Almost all of us will be fooled by things created by AI. Another site is fun for kids as it shows very realistic AI-created cats that don't really exist. And another site at is also a human vs AI activity where you decide whether art, music, writing or photos were created by a human or AI.

All of those examples can be used as a way to introduce students to how AI is used and even caution them to recognize that they can be not only helped but deceived using AI.