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Saturday, November 28, 2020

A New, Free, MIT Introduction to Machine Learning AI Course

We worked with MIT in a number of ways, with their supply chain group,  their Media Laboratory and with a number of their researchers.  Impressive group.  I still provide publicity for some of their research. We introduced early AI to the enterprise.  I have now been asked to provide a review of their latest. A new, Free Introduction to Machine Learning AI Course.    See more more about it below.   If you also take it please provide some of your own thoughts about it to me.  - Franz  

MIT Released a New, Free Machine Learning Course

Don’t miss this!  By Frederik Bussler from Medium

MIT Open Learning Library just released an Introduction to Machine Learning course    . The free 13-week course covers machine learning algorithms, supervised and reinforcement learning, and more.

While you don’t need these skills to deploy AI, given no-code AI companies like Obviously.AI   , it can help if you want to work on the cutting-edge and build new architectures.

Why MIT?

MIT is a world-famous organization, and for a good reason. This prestigious university boasts graduates like Buzz Aldrin, the second person to walk on the moon (fun fact: Buzz Lightyear was named after Buzz Aldrin).

You’ll be part of a community of learners. Dreamers. Doers. Indeed, part of the Open Learning Library’s mission is “Extending MIT’s knowledge to the world.” You get all this for free.  ....

Why Bother Learning It?

Nowadays, AutoML tools seem to be taking over the world. If you Google “AI without code,” you’ll find tools like RunwayML for creative applications and Obviously.AI for tabular data, besides the more well-known Google AutoML tools (though, to be fair, Google’s version is fairly hard to use).

Given that AI today is easier to do than ever before, why bother taking the course? The answer is that AutoML supplements creative thinking but doesn’t replace it. Courses like MIT’s Intro to ML will help you understand good use-cases for the technology, as well as when to use AI or something else.

By laying a foundation of understanding, you can then use AutoML tools to extremely quickly create machine learning predictions. By deploying models quickly and easily, you can focus on creativity and intuition instead of lower-level work and dealing with code bugs.

What Should You Do Next?

A huge part of online learning is execution. Many people take online courses, share the certificate on their profile, and move on. While it’s great to let others know that you’re learning, you want to use the course as a stepping stone — as part of the journey, not the destination.

I encourage you to not only sign up for the course but to plan for what you’ll do after. A great way to execute what you’ve learned is to visit Kaggle Datasets and find some interesting data that you want to know more about. Then, you could try one of the aforementioned AutoML tools and deploy your knowledge.

Another great way to execute is to look more closely to home and ask how you can help your own organization. What data do you have available? What business challenges are you facing? Are there KPIs in your data that you can analyze? Even if you’re still a student, you could look and ask to see how you can use data to improve outcomes at your college or university. .... 


The world is changing quickly. To stay up-to-date and current with technology, you need to be a lifelong learner.   Given how fast the AI industry changes, taking a several-year-old course just may not cut it, so looking for new courses is a great way to stay ahead.   Ultimately, MIT is a prestigious, renowned organization for good reason, and their free ML course is a no-brainer. Just make sure you use what you learned for good!  ..... 

I’ve signed up — how about you?    .... "   By Frederik Bussler

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