Machine learning falls within the realm of artificial intelligence (AI), concentrating on designing algorithms and models that empower computers to learn from data and make predictions or decisions, all without the need for explicit programming.
There are three categories of machine learning:
Supervised Learning: This approach involves training the machine using labeled data, where examples have predefined outcomes.
Algorithms in this category include Linear Regression, Logistic Regression, and Support Vector Machine.
Unsupervised Learning: Here, the machine is trained on unlabeled data without any predefined guidance.
Algorithms in this category encompass K Means Clustering, Hierarchical Clustering, and Principal Component Analysis.
Reinforcement Learning: This type revolves around an agent interacting with its environment, making actions and learning from errors or rewards.
Algorithms here include Q-Learning, Deep Q Network, and SARSA.
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