To make technical content engaging for beginners, I break it down into bite-sized, interactive lessons with real-world analogies, like comparing loops to recipes, to build intuition without overwhelming them. As a Coddy course creator, I incorporate hands-on coding challenges with instant feedback via integrated compilers, gamification elements like badges, and storytelling to weave concepts into relatable narratives. During Fiverr tutoring, I used visuals, step-by-step debugging demos, and personalized examples tied to their interests, ensuring they stay motivated and grasp fundamentals progressively.

I am most comfortable with Python, which I use for machine learning. I'm also proficient in Dart through Flutter for mobile app development, then C++ for algorithms and competitive programming, and Java for some software engineering tasks. But, I have mostly created courses for C++.

When debugging a complex AI application, I start by isolating the issue through modular testing, breaking the pipeline into components like data ingestion, preprocessing, feature engineering, model training, and evaluations. I use logging extensively with Python's logging module or print statements for insights, and tools like Jupyter notebooks for interactive exploration. For ML-specific bugs, I inspect data distributions with Pandas and visualizations via Matplotlib, and verify model architectures in TensorFlow. I check for common pitfalls like overfitting by cross-validating, optimize hyperparameters iteratively, and leverage Git for version control to revert changes.