Difference Between Machine Learning and Deep Learning

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 themselves.

Deep Learning: Deep Learning is a subset of Machine Learning where the artificial neural network, the recurrent neural network comes 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 as 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, exactly is the concept of deep learning. It solves all the complex problems with the help of algorithms and its process.

Machine-Learning-vs-Deep-Learning
Below is a table of differences between Machine Learning and Deep Learning:

S.No. Machine Learning Deep Learning
1. Machine Learning is a superset of Deep Learning Deep 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 data The data representation is used in Deep Learning is quite different as it uses neural networks(ANN).
3. Machine Learning is an evolution of AI Deep Learning is an evolution to 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 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 the 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.
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