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What is Reinforcement Learning?

Reinforcement learning refers to a type of machine learning where an agent learns optimal behaviors by interacting with an environment through rewards and penalties. It is widely used in robotics, gaming, finance, and autonomous systems.

Local differential privacy
Local differential privacy is a privacy-preserving technique that adds noise to individual data before it is shared or analyzed, ensuring anonymity without relying on centralized data aggregation. It is commonly used in privacy-focused analytics by companies like Apple and Google to collect user data while minimizing exposure risks.
AI Agents
AI agents are autonomous systems that perceive their environment, make decisions, and take actions to achieve specific goals. They use artificial intelligence (AI) techniques such as machine learning, natural language processing (NLP), and reinforcement learning to adapt and improve their performance over time. AI agents are widely used in various applications, including chatbots, recommendation systems, robotic automation, and autonomous vehicles.
Data Science
Data science is an interdisciplinary field that combines statistical analysis, machine learning, and data engineering to extract insights from structured and unstructured data. It plays a crucial role in predictive analytics, business intelligence, and AI development. Data scientists use tools like Python, R, and TensorFlow to analyze and interpret data for decision-making.

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