Explain the key differences between Machine Learning and Deep Learning.

Machine Learning (ML) and Deep Learning (DL) are both subfields of Artificial Intelligence (AI), but they differ significantly in their approach, complexity, and capabilities. Machine Learning is a broader field that encompasses various algorithms that allow computers to learn from data without being explicitly programmed. These algorithms can identify patterns, make predictions, and improve their performance over time as they are exposed to more data. Traditional ML algorithms, such as decision trees, support vector machines, and logistic regression, typically require feature engineering, meaning that a human expert must manually select and extract the relevant features from the data that are most predictive of the outcome. Deep Learning, on the other hand, is a specific type of Machine Learning that uses artificial neural networks with multiple layers (hence 'deep') to analyze data. These deep neural networks can automatically learn features from the data without requiring explicit feature engineering. This is a key advantage of Deep Learning, as it can handle complex and high-dimensional data, such as images, audio, and text, more effectively than traditional ML algorithms. Furthermore, Deep Learning models typically require much larger datasets and more computational power than traditional ML models. A simple example: classifying images. With machine learning, you would have to manually determine what image attributes (edges, shapes, colors) are used to classify the image. With deep learning, the algorithm learns which attributes are important itself. In short, deep learning is a type of machine learning, which removes manual feature identification.