As I embark on my journey as a Machine Learning engineer, I’m seeking guidance on the best path forward. With a background in Control System engineering and a wealth of experience in system modeling and controls, I’ve dedicated the past year and a half to learning the fundamentals of ML.
I’ve completed several specializations, including Andrew Ng’s classic course, and have worked on projects involving RNNs, CNNs, and time series analysis. I’m familiar with programming languages like C, Matlab, and Python, and have a solid grasp of calculus, linear algebra, and basic statistics.
My projects on GitHub and Google Collab demonstrate my ability to implement ML concepts, and I’ve dabbled in areas like reinforcement learning, LLM, and XGBoost. But now, I’m unsure about the next steps.
Should I Specialize or Generalize?
One of my biggest questions is whether I should focus on a specific domain within ML or continue to explore various areas. While I find most ML topics fascinating, I’m aware that specializing in one area might increase my chances of landing a job.
Which Subdomain of ML Has the Most Job Prospects?
Reinforcement learning, with its connections to robotics, seems like a promising area to specialize in. However, I’m curious to know if other subdomains, like computer vision or natural language processing, are currently in high demand.
Collaboration and Building a Portfolio
I’m considering teaming up with others on Kaggle or Reddit to work on projects and build my portfolio. But I’m unsure if this is the right approach, or if I should focus on applying for jobs and seeing what happens.
Does Anyone Care About My Online Presence?
I’m also wondering if my online presence, including my Medium articles and Kaggle competitions, will make a difference in my job search.
The Power of Partnerships
Lastly, I’m looking to partner with others who share my interests in ML and AI. If you’re interested in working on projects together, let’s connect!
Thanks for taking the time to read my post and offer your guidance.