Functions, loops, list comprehensions, classes
Error handling (try-except), logging
File I/O operations, working with JSON/CSV
Python Libraries for AI/ML:
numpy, pandas - Data manipulation & analysis
matplotlib, seaborn - Data visualization
scikit-learn - Classical machine learning models
Basic familiarity with tensorflow or pytorch
Working knowledge of Openai / Transformers (bonus)
AI/ML Fundamentals:
Supervised and unsupervised learning (e.g., regression, classification, clustering)
Concepts of overfitting, underfitting, and bias-variance tradeoff
Train-test split, cross-validation
Evaluation metrics: accuracy, precision, recall, F1-score, confusion matrix
Data Preprocessing: