Machine learning
Machine Learning training typically begins with a strong foundation in the basic concepts of AI and machine learning. This includes understanding the core types of ML algorithms, such as supervised learning, unsupervised learning, and reinforcement learning.
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Benefits of ML (Machine Learning) Training
Machine Learning: Models are trained on labeled data, learning from input-output pairs (e.g., regression and classification problems).
Unsupervised Learning: The algorithm tries to find hidden patterns or structures in data without pre-labeled outputs (e.g., clustering, association).
Reinforcement Learning: Agents learn to make decisions by receiving rewards or penalties based on their actions, often applied in environments like robotics or game theory.
Key Libraries and Framework: NumPy, Pandas, and Matplotlib: For data manipulation and visualization.
Scikit-learn: A versatile library for implementing machine learning algorithms (regression, classification, clustering).
