Hello everyone.
I have graduated from the university and am working as a software developer in an organization. I am interested in both Machine Learning and Software Engineering, but ML applications fancy me more because of the diverse nature and the impact on environment and humanity as a whole.
Say I want to work as an ML Engineer later on in my career. So how could I transition from a SWE to ML Engg? Would my experience as a SWE count as well? Also, companies more often than not have this “experience” requirement in the job description. How can I navigate that?
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Hi @UsamaHussain
Yes, your SE will help you a lot in the MLE journey, because you already has knowledge about programming and software management. So, what you can do is to start learning about ML. You can start getting the Machine Learning Specialisation, which is an excellent course. In addition, keep your GitHub repository updated and try to contribute to open-source projects. Have success in your career transition.
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I agree with @carlosrl. SW engineering gives you a great basis for machine leaning.
The MLS courses will give you the introduction to some simple methods and concepts.
DLS (Deep Learning Specialization) will give you experience in more complicated methods on a number of topics.
After that you can study in other areas of interest in more depth.
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ok great. I have already covered 2 modules of the total 3 in ML Specialization, and enjoyed and learned a lot. I also know basics about CNNs, RNNs, LSTM and used Transformers in Fake News Detection (was quite complicated).
Does Kaggle help in this endeavour? I am specially concerned about the experience that companies demand (and will my SWE experience be considered)?
Sorry, I cannot give career advice.
Kaggle is a good source of data sets you can work on.