Comparative Analysis of Shallow Learning and Deep Learning

Artificial Intelligence (AI) has numerous real-world applications in various fields and industries. It plays a significant role in different aspects of everyday life and has the potential to transform industries. AI technologies are constantly improving, and their impact on society and daily life is growing considerably. Machine learning has diverse applications in fields such as healthcare, agriculture, animal husbandary, education, and also works in environment-friendly systems for the economic welfare of society. Shallow and deep learning are two sub-branches of machine learning that help make decisions and future predictions. Both have their scope and role in data analytics. This paper presents the basics of machine learning and compares shallow and deep learning models while discussing their various models separately. Furthermore, a literature review demonstrates the impact of both in various fields in real-life applications. Given insights of shallow learning and deep learning are presented along with their applications. Comparative analysis is done with shallow learning's linear regression and deep learning's CNN sequential model on a dataset of gold price increasing and decreasing day by day. CNN sequential model performs better with overall 83% accuracy. It may help the researchers to have a clear understanding of ML and DL.