What is the difference between machine learning and deep learning?

Machine Learning and Deep Learning are both subsets of artificial intelligence, but they differ primarily in their approaches and complexity:
1. Model Structure:
* Machine Learning: Uses simpler models such as linear regression, decision trees, or support vector machines. These models often require manual feature extraction and domain expertise.
* Deep Learning: Uses neural networks with many layers (deep networks), such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs). These models can automatically learn features from raw data through multiple layers of abstraction.
2. Feature Engineering:
* Machine Learning: Typically involves manual feature extraction and engineering, where domain knowledge is used to select and transform features.
* Deep Learning: Automatically extracts and learns features from raw data, often reducing the need for manual feature engineering.
3. Data Requirements:
* Machine Learning: Often performs well with smaller datasets and less computational power.
* Deep Learning: Generally requires large amounts of data and significant computational resources (e.g., GPUs) to achieve high performance.
4. Applications:
* Machine Learning: Commonly used in applications like fraud detection, recommendation systems, and traditional predictive modeling.
* Deep Learning: Excels in tasks such as image recognition, natural language processing, and complex pattern recognition, where hierarchical feature learning is beneficial.