Internet of Things (IoT) Practice Exam 2026 – Comprehensive Prep Resource

Machine learning is a subset of artificial intelligence focused on the development of algorithms that enable computers to learn from and make predictions or decisions based on data. This process involves training models on large datasets, allowing them to recognize patterns, improve their accuracy over time, and essentially "learn" without being explicitly programmed for every specific task.

Machine learning encompasses various techniques and approaches, such as supervised learning, unsupervised learning, and reinforcement learning, which all leverage data to enhance the system's performance. This adaptability is fundamental to AI applications across numerous fields, including image recognition, natural language processing, and predictive analytics.

In contrast, data mining involves the analysis of large datasets to discover patterns and relationships within the data, but it does not inherently involve the learning aspect that characterizes machine learning. Predictive analytics uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data, but it is a broader application that references the use of machine learning. Natural language processing, while a significant area within AI, specifically focuses on the interaction between computers and human language and includes tasks such as speech recognition and sentiment analysis.