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Difference Between Machine Learning and Deep Learning

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  • Difficulty Level : Medium
  • Last Updated : 02 Aug, 2022
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Machine Learning: Machine learning is a subset, an application of Artificial Intelligence (AI) that offers the ability to the system to learn and improve from experience without being programmed to that level. Machine Learning uses data to train and find accurate results. Machine learning focuses on the development of a computer program that accesses the data and uses it to learn from itself.

Deep Learning: Deep Learning is a subset of Machine Learning where the artificial neural network and the recurrent neural network come in relation. The algorithms are created exactly just like machine learning but it consists of many more levels of algorithms. All these networks of the algorithm are together called the artificial neural network. In much simpler terms, it replicates just like the human brain as all the neural networks are connected in the brain, which exactly is the concept of deep learning. It solves all the complex problems with the help of algorithms and its process.


So, if you want to give Machine Learning a shot then you can also try GeeksforGeeks Machine Learning Basic and Advanced – Self-Paced course and get a hands-on experience. Mentored by industry experts this self-paced course will provide you with clarity on every concept.  Now let’s look at the difference between Machine Learning and Deep Learning: 

S. No.Machine LearningDeep Learning
1.Machine Learning is a superset of Deep LearningDeep Learning is a subset of Machine Learning
2.The data represented in Machine Learning is quite different as compared to Deep Learning as it uses structured dataThe data representation is used in Deep Learning is quite different as it uses neural networks(ANN).
3.Machine Learning is an evolution of AIDeep Learning is an evolution of Machine Learning. Basically, it is how deep is the machine learning.
4.Machine learning consists of thousands of data points.Big Data: Millions of data points.
5.Outputs: Numerical Value, like classification of the score.Anything from numerical values to free-form elements, such as free text and sound.
6.Uses various types of automated algorithms that turn to model functions and predict future action from data.Uses neural network that passes data through processing layers to, interpret data features and relations.
7.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.
8.Machine Learning is highly used to stay in the competition and learn new things. Deep Learning solves complex machine learning issues.
9.Training can be performed using the CPU (Central Processing Unit).A dedicated GPU (Graphics Processing Unit) is required for training.
10.More human intervention is involved for getting results.Although more difficult to set up, deep learning requires less intervention once it is running.
11.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).
12.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.
13.Humans explicitly do feature engineering.Feature engineering is not needed because important features are automatically detected by neural networks.
14.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.
15.The results of an ML model are easy to explain.The results of deep learning are difficult to explain.
16.Machine learning models can be used to solve straightforward or a little bit challenging issues. Deep learning models are appropriate for resolving challenging issues.
17.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. 
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