Seeking Advice: Leveraging ML Skills for Real-World Impact

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Hi everyone,

I’m looking to leverage programming skills, especially in machine learning, to solve real-world problems. My background is a bit eclectic - an engineering degree in IoT, a stint as a professional poker player, and experience in algorithmic trading. Recently I’ve been the only tech guy in a very small team, developing everything from trading bots to considering ML applications in trading strategies.

However, I’m yearning to shift gears and make a tangible impact. My journey into ML has sparked a desire to apply these skills beyond the confines of my current work, particularly in areas that directly enhance people’s lives, like optimizing traffic flows, improving cybersecurity, enhancing renewable energy utilization, or making government spending more efficient.

I’m proficient in Python, have a grasp on networking, some experience with AWS, and a foundational understanding of math and stats. Currently taking Machine Learning Engineering for Production (MLOps) Specialization.

Given this context, I’m reaching out for guidance on potential paths that align with my aspiration to see the real-world effects of my work. I value flexibility and wish to avoid roles where my contributions feel diluted or focused on less impactful areas like user retention optimization.

Any suggestions on how to navigate this transition, including job types to consider or skills to prioritize, would be greatly appreciated. I’m cautious of spreading myself too thin yet hesitant to over-specialize without a clear target.

Thanks for your insights

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Hello @MX1000

Do you know Kaggle Competitions ?
Based on what you shared I guess that may be a good way to develop skills and find something useful/impactful what you can involve yourself.
I do not have anything with Kaggle but I think the concept is interesting and promising. Hopefully valuable also for you.

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I did hear about it, sounds like a great way to get involved in many different applications of ML. Thanks for sharing, will look deeper into it!

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You might also benefit from the Deep Learning Specialization. The first couple of courses might be review, but that’s always a good use of your time, especially if you haven’t used TensorFlow before.

The remainder of the course is strong on things you may not have used yet (Convolutional Neural Networks and Sequence Models).

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Thanks for the advices ill have to make a list of the next courses to follow. For now I split my time 50/50 between courses and building projects so I can drill what I learn

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Sounds very reasonable - I’ve heard a phrase that I’m not sure always applies, but in this case it looks valid:

The purpose of education is to act

Keep fingers crossed for your theory & practice combination :slight_smile:

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