What are the challenges in machine learning?

Machine learning, despite its tremendous potential, is not without challenges. One major hurdle is the lack of high-quality and diverse training data. Obtaining sufficient and representative data can be difficult and time-consuming, leading to biased or incomplete models. Another challenge is feature engineering, as identifying the most informative features for a particular problem is often a labor-intensive task. Moreover, machine learning models can be prone to overfitting, where they memorize the training data rather than learning the underlying patterns. Balancing bias and variance poses yet another challenge wherein models can be either too simple or too complex. Additionally, interpreting and explaining the decisions made by machine learning models, especially in deep learning, is a challenging task that limits their applicability in critical domains. Lastly, the rapid advancements in machine learning techniques require continuous learning and upskilling to keep up with the constantly evolving field.

This mind map was published on 9 January 2024 and has been viewed 95 times.