Comparative Insights into Machine Learning and Deep Learning Models: Applications and Performance

Abstract

  This paper compares ML and DL, two key areas of AI, to understand their strengths and weaknesses and facilitate model selection for different applications. ML includes approaches such as DT and SVM, which are suitable for smaller datasets and simpler tasks, while DL uses neural networks to tackle large datasets and complex tasks such as image detection & NLP. We evaluate their performance based on accuracy, speed and interpretability. We find that ML models are often faster and easier to understand, while DL models excel in accuracy for complex problems. Practical applications in industries like healthcare, finance & retail are examined, demonstrating the effectiveness of ML on predictive tasks and the superiority of DL on tasks requiring detailed data analysis. This study provides insights to help scholars select the appropriate AI models for particular demands and thus improve the application of AI in solving real-world challenges.