Deep learning is a subset of machine learning, and the primary distinction lies in the architecture and complexity of the models used.

Scope:

Machine Learning (ML): It is a broader concept that encompasses a variety of algorithms and techniques allowing computers to learn from data and make decisions or predictions.
Deep Learning (DL): It is a specific type of machine learning that involves neural networks with multiple layers (deep neural networks). Deep learning focuses on automatically learning hierarchical representations of data.
Representation of Data:

Machine Learning (ML): Typically relies on feature engineering, where human experts manually select and design relevant features from the input data.
Deep Learning (DL): Learns hierarchical representations directly from raw data, eliminating the need for extensive manual feature engineering.
Model Complexity:

Machine Learning (ML): Uses a variety of algorithms such as decision trees, support vector machines, k-nearest neighbors, etc. These algorithms may have simpler structures compared to deep neural networks.
Deep Learning (DL): Employs deep neural networks with multiple layers (deep architectures). These networks can automatically learn intricate patterns and representations from data, making them well-suited for complex tasks.
Training and Computation:

Machine Learning (ML): Training models may require less computational power compared to deep learning models.
Deep Learning (DL): Training deep neural networks often demands significant computational resources, and GPUs or specialized hardware are commonly used to accelerate the process.
Task Types:

Machine Learning (ML): Applies to a wide range of tasks, including classification, regression, clustering, and more.
Deep Learning (DL): Particularly excels in tasks like image and speech recognition, natural language processing, and tasks involving large amounts of complex data.
Read More... https://bit.ly/3NI3dCT