A Comprehensive Review of Machine Learning Algorithms and TheirDiverse Applications

This paper reviews diverse machine learning algorithms and their applications. It covers supervised learning, exploring Linear Regression, Decision Trees, and Support Vector Machines for tasks like regression and classification. Unsupervised learning is discussed, including K-Means and Hierarchical Clustering for clustering and anomaly detection. Reinforcement learning is outlined with algorithms like Q-Learning and Deep Q Networks in gaming and robotics. Deep learning is emphasized, addressing neural networks and convolutional neural networks in image and speech recognition. Ensemble methods like Random Forest and Gradient Boosting are examined. Industry-specific applications in healthcare, finance, and marketing are explored. The paper concludes with challenges, ethics, and future trends in machine learning.

Keywords:

supervised learning , unsupervised learning , reinforcement learning , neural networks , ensemble learning , ethical consideration