Answer: Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, including learning, reasoning, and problem-solving. AI can be classified into narrow AI, which is designed for specific tasks, and general AI, which aims to perform any intellectual task that a human can do.

Answer: Machine Learning (ML) is a subset of AI that enables systems to learn from data, identify patterns, and make decisions without being explicitly programmed. ML algorithms improve their performance as they are exposed to more data. Common types of ML include supervised, unsupervised, and reinforcement learning.

Answer: AI is the broad field that encompasses machines designed to perform tasks that require human intelligence. Machine Learning (ML) is a subset of AI that allows machines to learn from data. Deep Learning (DL) is a subset of ML that uses neural networks with many layers to analyze large datasets and solve more complex problems, such as image and speech recognition.

Answer: Supervised learning is a type of machine learning where the algorithm is trained on labeled data. The system learns to map input data to the correct output using the labeled training data. It's commonly used for tasks like classification and regression. Examples include email spam detection and medical diagnosis.

Answer: Unsupervised learning is a type of machine learning where the algorithm is given data without labeled outcomes. The model tries to find patterns or groupings in the data. Common algorithms for unsupervised learning include clustering (e.g., k-means) and dimensionality reduction (e.g., PCA).

Answer: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions and receiving feedback in the form of rewards or penalties. The goal is to learn a strategy that maximizes long-term rewards. It is commonly used in robotics, gaming, and autonomous systems.

Answer: Overfitting occurs when a machine learning model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data. The model becomes too complex and fails to generalize well to unseen data. Regularization techniques like L2 regularization and dropout can help prevent overfitting.

Answer: A neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. They are used in deep learning to model complex patterns and behaviors and are essential for tasks such as image recognition and natural language processing.

Answer: Data preprocessing is a crucial step in the machine learning pipeline. It involves cleaning and transforming raw data into a format suitable for modeling. Common preprocessing tasks include handling missing data, scaling features, encoding categorical variables, and splitting data into training and testing sets. Proper preprocessing ensures the model performs well and avoids biases.