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How would one self-study enough about ML to be able to move into an ML engineering role without an academic background on it? Any recommended paths out there?
by exdsq 5y ago
How would one self-study enough about ML to be able to move into an ML engineering role without an academic background on it? Any recommended paths out there?
- bckr 5y agoI corresponded with another user here who accomplished this and wrote about it here[] []http://karlrosaen.com/ml/ http://karlrosaen.com/ml/
- sriram_malhar 5y agoI suggest starting with Andrew Ng's Deep Learning Specialization set of courses. It is a very decent overview.
- kajecounterhack 5y agoI would recommend joining an ML-centric company in an ML infrastructure role, and get real-world experience that way. (Example industries to look at: self driving car companies, spam & abuse departments of major companies, data labeling firms, ML consulting firms). Ideally you want to work with as many ML engineers as you can. Classes and independent study are great, but a lot of these companies want to hire experienced folks for ML roles, so once you have picked up some basics from independent study it's helpful to get an _ML adjacent_ role to help you start moving laterally toward the ML engineer position you want.
- throwaway81523 5y agoThe fast.ai videos are good though very long. Expect to spend a lot of time on the exercises, and you will need an nvidia gpu-equipped computer unless something has changed recently. Those are available as cloud rentals of course though.
- jpalomaki 5y agoStart from simple, take some scikit-learn tutorials and start building predictive models. Look for some real problems, maybe from area that interests you. Sports, weather, stock market. Set a goal for what you want to predict, look for data and then Google and go with trial and error. You don’t really need academic degree on the topic to do productive work with these tools. Also spend time with learning the related non-ml tools like Pandas and some Numpy. Understanding Pandas is (IMHO) key to get anything done and unfortunately it is not too simple to get around.
- mark_l_watson 5y agoI would find a few things that you are interested in, write deep learning models around these areas of interests (it is OK to clone similar Keras or PyTorch examples and modify/extend them). Then, bravely apply for deep learning jobs, admit to no professional experience but point to your models on GitHub and be ready to be able to carefully explain what you did, how you worked around problems, etc. Be willing to take an entry level position. Also, learn to be flexible about spending a lot of effort learning whatever domain your future employers work in. Learn their businesses, how they operate, what data they have, etc.
- magicalhippo 5y ago> write deep learning models around these areas of interests I've read tons of articles on how to assemble models using PyTorch etc, but very little on how to design them. Like overall architecture to use for what problems, conditioning of input data, number of layers, width of layers at each depth. I haven't read this book yet, so maybe this has some on that, but are there any other decent resources that go a bit into this? At least something that gives some overall pointers, I'm guessing this can get quite involved quite fast.
- mark_l_watson 5y agoYou might look through the Keras examples https://keras.io/examples/ https://keras.io/examples/ Find Somerville to your application.