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Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) 

Artificial Intelligence (AI): Artificial Intelligence is a broad field of computer science that aims to create machines or systems that can perform tasks that would typically require human intelligence. These tasks may include problem-solving, understanding natural language, recognizing patterns, learning from experience, and more. AI encompasses a wide range of techniques and approaches, including machine learning and deep learning. 

Machine Learning (ML): Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Instead of being explicitly programmed to perform a specific task, machine learning algorithms use data to learn patterns and relationships, allowing them to improve their performance over time. Machine learning techniques include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. 

Deep Learning: Deep Learning is a subfield of machine learning that is inspired by the structure and function of the human brain, particularly the interconnected network of neurons. Deep learning algorithms, known as neural networks, consist of multiple layers of interconnected nodes (artificial neurons) that process data and extract features at increasingly abstract levels. Deep learning has achieved remarkable success in various tasks such as image recognition, natural language processing, and speech recognition, thanks to its ability to automatically learn hierarchical representations from large amounts of data. In summary, AI is the overarching field that encompasses techniques such as machine learning and deep learning. Machine learning is a subset of AI focused on algorithms that learn from data, while deep learning is a subset of machine learning that uses deep neural networks to learn intricate patterns from large datasets.