| Machine Learning is a superset of Deep Learning | Deep Learning is a subset of Machine Learning |
| The data represented in Machine Learning is quite different compared to Deep Learning as it uses structured data | The data representation used in Deep Learning is quite different as it uses neural networks(ANN). |
| Machine Learning is an evolution of AI. | Deep Learning is an evolution of Machine Learning. Basically, it is how deep is the machine learning. |
| Machine learning consists of thousands of data points. | Big Data: Millions of data points. |
| Outputs: Numerical Value, like classification of the score. | Anything from numerical values to free-form elements, such as free text and sound. |
| Uses various types of automated algorithms that turn to model functions and predict future action from data. | Uses a neural network that passes data through processing layers to, interpret data features and relations. |
| Algorithms are detected by data analysts to examine specific variables in data sets. | Algorithms are largely self-depicted on data analysis once they're put into production. |
| Machine Learning is highly used to stay in the competition and learn new things. | Deep Learning solves complex machine-learning issues. |
| Training can be performed using the CPU (Central Processing Unit). | A dedicated GPU (Graphics Processing Unit) is required for training. |
| More human intervention is involved in getting results. | Although more difficult to set up, deep learning requires less intervention once it is running. |
| Machine learning systems can be swiftly set up and run, but their effectiveness may be constrained. | Although they require additional setup time, deep learning algorithms can produce results immediately (although the quality is likely to improve over time as more data becomes available). |
| Its model takes less time in training due to its small size. | A huge amount of time is taken because of very big data points. |
| Humans explicitly do feature engineering. | Feature engineering is not needed because important features are automatically detected by neural networks. |
| Machine learning applications are simpler compared to deep learning and can be executed on standard computers. | Deep learning systems utilize much more powerful hardware and resources. |
| The results of an ML model are easy to explain. | The results of deep learning are difficult to explain. |
| Machine learning models can be used to solve straightforward or a little bit challenging issues. | Deep learning models are appropriate for resolving challenging issues. |
| Banks, doctor's offices, and mailboxes all employ machine learning already. | Deep learning technology enables increasingly sophisticated and autonomous algorithms, such as self-driving automobiles or surgical robots. |
| Machine learning involves training algorithms to identify patterns and relationships in data. | Deep learning, on the other hand, uses complex neural networks with multiple layers to analyze more intricate patterns and relationships. |
| Â Machine learning algorithms can range from simple linear models to more complex models such as decision trees and random forests. | Deep learning algorithms, on the other hand, are based on artificial neural networks that consist of multiple layers and nodes. |
| Machine learning algorithms typically require less data than deep learning algorithms, but the quality of the data is more important. | Deep learning algorithms, on the other hand, require large amounts of data to train the neural networks but can learn and improve on their own as they process more data. |
| Machine learning is used for a wide range of applications, such as regression, classification, and clustering. | Deep learning, on the other hand, is mostly used for complex tasks such as image and speech recognition, natural language processing, and autonomous systems. |
| Machine learning algorithms for complex tasks, but they can also be more difficult to train and may require more computational resources. | Deep learning algorithms are more accurate than machine learning algorithms. |