Machine learning challenges

Machine learning faces several challenges, including ensuring the quality and quantity of data—if data is biased or insufficient, the system’s accuracy suffers. It requires complex algorithms and significant processing power, which can be costly. Overfitting happens when a model learns noise instead of patterns, limiting its usefulness on new data. Transparency is also a concern; understanding how decisions are made isn’t always straightforward. Additionally, ethical issues, such as privacy and fairness, need careful handling to prevent unintended harm. Addressing these challenges is essential for developing reliable and trustworthy machine learning applications.