A Review of Machine Learning Techniques: Basic Algorithms and Use Cases

Machine learning (ML) has become a cornerstone of modern data-driven technologies, providing systems the ability to automatically learn and improve from experience without being explicitly programmed. This paper reviews the basic machine learning techniques and algorithms, including supervised, unsupervised, and reinforcement learning. Key algorithms such as linear regression, decision trees, support vector machines (SVM), k-means clustering, and neural networks are discussed. The paper also explores various real-world use cases of machine learning across industries such as healthcare, finance, marketing, and transportation, providing a comprehensive overview of its impact and potential challenges.