1. Architecture and Complexity:

• Machine Learning (ML): In traditional machine learning, models often rely on manually crafted features, and the algorithms are typically simpler. The feature extraction and selection process requires domain expertise.
• Deep Learning (DL): Deep learning models, specifically neural networks, are characterized by their deep architecture with multiple layers of interconnected nodes. Deep learning algorithms automatically learn hierarchical features from data, eliminating the need for manual feature engineering.

2. Data Requirements:

• Machine Learning (ML): ML models generally perform well with smaller datasets and rely heavily on feature engineering. They may struggle to extract complex patterns from large volumes of raw data.
• Deep Learning (DL): Deep learning thrives on large volumes of labeled data. The inherent hierarchical feature learning capability allows DL models to automatically discover intricate patterns from raw data, making them exceptionally effective with big datasets.

3. Task Specificity:

• Machine Learning (ML): ML models are often designed for specific tasks, and creating a new model for a different task may require a significant amount of manual tweaking and reengineering.
• Deep Learning (DL): Deep learning models, particularly deep neural networks, are more versatile. They can be applied to a broad range of tasks without substantial modification. Transfer learning techniques enable pre-trained models to be fine-tuned for various applications, reducing the need for task-specific architectures.

Understanding these differences helps practitioners choose the right approach based on the nature of the task, available data, and the desired level of automation and complexity (ChatGPT, 2024).