Insights into Machine Learning and Deep Learning Approches: A Systematic Review

Machine Learning (ML) & Deep Learning (DL) are transformative technologies that have revolutionized various fields, from healthcare and finance to Natural Language Processing (NLP) & computer vision. These strategies provide both considerable obstacles along with chances for additional advancement and study despite of various uses. This research aims to provide comprehensive review of various ML & DL approaches by focusing on their methodology, fundamental advancements and evolving trends. Conventional ML techniques like Support Vector Machine (SVM), Decision Tree and K- Nearest Neighbor (KNN) in addition with more recent developments in deep learning like Convolutional Neural Network (CNN) & Recurrent Neural Network (RNN) are explored. The paper also highlights various issues like overfitting, scalability and computational complexity which challenge the accuracy of the models. Finally, the future scope and potential areas of innovation are discussed along with AI impact on the next generation intelligent system in AutoML. The review aims to provide valuable insights for researchers to navigate various evolving machine learning and deep learning classifiers.