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How to get up to speed on Machine Learning and AI

Aug 27, 2019 · 4 min read

By Oren Etzioni and the AI2 Team

At AI2, we have liberal doses of AI and machine learning with our coffee. So, we are often asked for resources on AI by both technical and non-technical friends eager to learn more about this hot topic. It’s not that our friends can’t do a Google search — there are too many resources out there, and it’s hard to tell which are good and which are confusing; what serves as a gentle introduction, and what’s for more advanced interest.

In response, we’ve put together a brief list of high-quality resources. We’ve erred on the side of brevity instead of completeness. However, we are maintaining a “living document” of these resources at AI2, so please make recommendations in the comments section, and we will update and refine the list over time.

Engineers can scroll down to find technical resources in the next section.

Non-Technical Resources

That summary is coupled with a crisp overview of Machine Learning terminology: What is Machine Learning?

Both are authored by Karen Hao and feature elegant flow charts to guide your understanding.

For a comprehensive dive into AI and its applications, we recommend Andrews Ng’s AI for Everyone Coursera series.

To cut through some of the hype surrounding AI

If you want more depth, this Harvard Business Review article by MIT Professors Erik Brynjolfsson and Andrew McAfee is an insightful and elegantly written overview: The Business of Artificial Intelligence

On regulatory and ethical issues, we recommend the following:

Further reading

Technical Resources for Engineers

For more depth, we recommend this insightful review article by UW Prof. Pedro Domingos: A Few Useful Things to Know about Machine Learning

For developing your own machine learning skills

To delve specifically into Deep Learning, we recommend:

Stanford and CMU make their course materials available online here:

* Stanford CS230 Deep Learning

* CMU CS 11–747 Neural Networks for NLP

Additional technical resources

We also recommend the hands-on book Data Science from Scratch: First Principles with Python by AI2’s Joel Grus. Be sure to get the second edition (and note the many positive reviews of the first one!).

Finally, here’s a practical note from a tweet by Joel:

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