I evaluate Python-certified candidates in AI and data science by assessing real project depth, production deployment experience, and how well they apply a python programming certification to solve business problems. At H2K Infosys, practical evaluations focus on Python workflows, ML pipelines, and data engineering fundamentals beyond theoretical knowledge.
Bullet-Point Breakdown:
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Portfolio strength: end-to-end projects using pandas, NumPy, scikit-learn, TensorFlow, or PyTorch
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Production readiness: Git version control, model deployment, and basic MLOps practices
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Analytical thinking: feature engineering, metrics selection, and performance evaluation
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Communication: explaining model limitations, bias, and tradeoffs clearly
Employers value candidates who can prove their Python skills work reliably in real, production-grade AI systems.