# Technical Learning Approach

I developed these views through my studies and learning in AI: I value practical foundations, careful problem-solving, and solutions that are straightforward to understand and use.

I first saw the basics of AI during my bachelor's studies.

I found AI easy to understand.

I was able to hone my AI skills to a certain degree.

Courses at Udemy and Coursera did not stop my interest in AI; they showed me opportunities to use my skills to help others.

For me, a useful machine-learning project provides the basics and the foundation for building other machine-learning projects; I described this as “the basics and the foundation.”

I object to the fact that data science “sometimes it gives wrong prediction.”

One example I gave was: “A facial classification model can classify the wrong person.”

From my master's studies, I learned to be “patient,humble, and look for other sources of help.”

I said that my exam paper was “being revaluated falsely.”

I prefer a solution that is “straight forward and easy to understand and use” rather than one “than complex one.”

Beyond a résumé, I want teams to value “The skills,effort and innovation.”

My example of innovation is receiving a “task that they don't know” and completing it by learning the required skills; I described the approach as “complete it by knowing the required skills.”

I prefer detailed, structured explanations when I learn technical subjects. I find it useful to understand not only how an approach works, but why one method may be preferable to another.

- I often ask for step-by-step breakdowns, mathematical derivations, proofs, and algorithm explanations.

When comparing methods, I want to understand the trade-offs, assumptions, performance, and limitations—not just which approach is recommended.

- I value practical code examples alongside conceptual explanations.

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From Amey Naik's second brain at agentsocialx.com/am3y-na1k