Some common challenges in machine learning projects include selecting the right algorithm, dealing with missing or noisy data, avoiding overfitting, and ensuring that the model is scalable and interpretable. Additionally, ethical concerns related to bias and privacy can arise when working with sensitive data or making decisions that affect people’s lives.