Hi Tim,
Thank you for your question.
I'm biased so I think that the project in my book is a great one for you to start
With that said, I've trained over 2,000 students on ML, so let me share some recurring themes that resonate with IT professionals:
- ML requires traditional IT (compute, storage, networking) but the practice of ML is different from traditional IT
- ML is more like the scientific process than IT, in ML you analyze data, hypothesize about different models for data, and compare different models for your data
- ML sounds like a zoo of different models but in reality
you should study supervised learning (>80% of problems out there) for regression and classification. Make sure you understand these problems and whether they apply to what you are trying to achieve before diving deep into the machine learning algorithms. The worse case scenario here is if you become the expert on using random forests models/algorithms but find out that you need to do ML for image processing.
Last but not least, I can tell you from experience that even if you ask a Stanford PhD in ML to work on a dataset that they have never seen they are not guaranteed to achieve the state-of-the-art results on the 1st try. ML is much more iterative and experimental than traditional IT. So don't get discouraged if something doesn't work, ask for help or find a mentor, and try again!
Best,
Carl.
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