Here is an update on my progress via an infographic.

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​https://infogram.io/p/8a587ec0b40db233371eac4d5d0e46ae.png

I have discovered more Machine Learning resources:

• Visual introduction to Machine Learning – very high level, but intuitive overview of machine learning. Use desktop computer to view it.  http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
• Machine Learning used in Google self-driving cars https://www.youtube.com/watch?v=lL16AQItG1g
• Machine Learning for Economics – I haven’t been able to watch these yet, but might be useful for somebody coming from Economics background. It does some translation between the terms used in economics field and in machine learning (thanks Olga for the link!) http://www.nber.org/econometrics_minicourse_2015/
• Heer is an interesting blog post, that lists machine learning resources depending on which machine learning “tribe” do you belong to, that is business person with general interest vs developer interested in delivering one-off predictions, etc. http://machinelearningmastery.com/machine-learning-tribe/

I am also reading book called Collective Intelligence. And although it is an older book it has a very intuitive intro into machine learning and it explains where it fits among all other methods of processing data and shows why is machine learning useful (complete with practical examples).

In contrast the Coursera course on Machine Learning starts off with much more math. In the end, both resources are very useful to learn from, but if I knew this from the beginning I would have read the Collective Intelligence book before starting the Coursera course.

Speech recognition though is still hard. I don’t understand most of the lectures. But that’s ok, even understanding just 10% is more helpful to doing my job better and understanding better what other people in my bigger team are doing.

I have learned a little more about linguistics. Ie what are heterographs and heteronyms.

Apple’s is ramping up its hiring of machine learning experts.

Fascinating, machine learning is used in all sorts of places: A machine learning algorithm picks out the fashion models most likely to succeed.