Machine learning faces several challenges that can impact its effectiveness. One major issue is the need for large amounts of high-quality data. If the data is biased or incomplete, the model may produce inaccurate results. Additionally, overfitting can occur when a model learns too much from the training data, making it less effective on new, unseen data. Another challenge is the interpretability of machine learning models. Many advanced models, like deep learning networks, operate as "black boxes," making it difficult for users to understand how decisions are made. This lack of transparency can hinder trust and adoption in critical areas such as healthcare and finance, where understanding the reasoning behind decisions is essential.