26 June 2024

Machine Learning Vs Deep Learning | Difference | Comparison

Numerous industries, including healthcare, retail, transportation, and banking, could be completely transformed by machine learning and deep learning, because they provide insights and automate decision making processes. 

In this blog post, we will learn Difference between Machine learning and deep learning. But before learning the differences, let first have brief introduction of machine learning and deep learning. 

What is Deep Learning?

Artificial neural network and recurrent neural network are related in deep learning, a subset of machine learning. The process of creating the algorithms is exactly the same as in machine learning, even though it has many more layers of algorithms. With this framework, a machine may analyse its own data and learn new things. It solves all complicated problems using its methods and algorithms.

What is Machine Learning?

Machine learning is a branch of artificial intelligence(AI) that allows a system to learn from its experiences and advance without needing to be fully programmed. The goal of machine learning is to create computer programmed that can access data and utilized it to learn from one another.  Machine learning algorithms is widely used in online fraud detection, production recommendation, email spam filtering and other areas. 

Machine Learning Vs Deep Learning | Difference between Machine Learning and  Deep Learning 

1. Subset

Machine Learning is a superset of deep learning, Deep learning is subset of Machine Learning.

2. Data Representation

The data representation in machine learning is quite different compared to deep learning as it uses structured data, while in the data representation used in deep learning is quite different as it uses neural networks.

3. Evolution

AI is the evolution of machine learning, while machine learning is the evolution of deep learning. In essence, it's the depth of the machine learning

4. Number f data points

Machine Learning can use small amounts of data to make predictions, On the other hand, Deep learning need to use large amount of training data to make predications.

5. Execution Time

Machine learning algorithm takes less time to train the model than deep learning, but it takes a long time duration to test the model, On the other hand, Deep learning takes a long execution time to train model, but less time to test the model. 

6. Hardware Dependency

Machine learning model  can work on low end machines, So doesn't need a large amount of computational power. While in Deep learning model needs to huge amount of data to work efficiently, do they need GPU's and high end machines. 

7.  Featurization Process

Machine learning model need a step of feature extraction by the expert, and then it proceeds further, On the other hand, Deep learning model learns high level of features from data and creates new features by it self, so it does not need to develop the feature extractor for each problem. 

8. Suitable for

Machine learning model are suitable for solving simple or bit complex problems, But deep learning models are suitable for solving complex problems.

9. Result

The result of an Machine learning model are easy to explain, The result of deep learning are difficult to explain.

10. Output

In machine learning the output is a numerical value, like a score or a classification, On the other hand, Deep learning the output can have multiple formats, like text, a score or a sound. 

11. Application

Machine learning is used for a wide range of applications, such as regression, classification and clustering. Deep learning is mostly used for complex tasks such as image and speech recognition, natural processing, and autonomous systems. 

12. Types of data

Machine learning model mostly require data in a structured form, on the other hand, Deep learning models can work with structured and unstructured data both as the rely on the layers of the artificial neural network.  

13. Problem solving approach

Traditional machine learning breaks the problems in sub parts, and after solving each part, produces the final result, The problem solving approach of a deep learning model is different from the traditional machine learning model, as it takes input for a given problem, and produce the end result. Hence it follows the end of end approach. 

14. Challenging Issues

Machine learning models can be used to solve straightforward or a little bit challenging issues, but the deep learning model are appropriate for resolving challenging issues. 

15. Complexity

Machine learning algorithms for complex tasks, but they can also be more difficult to train and may require more computational resources. Compared to machine learning algorithms, deep learning algorithms yield more accuracy.


In summary, deep learning can be defined as machine learning with additional functionalities and different methodology, and choose which of them to use to address a given issues, will rely on the problem complexity and data amount. 

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