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Artificial Intelligence

It is the study of training machines to mimic a human brain and its thinking abilities.
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Machine Learning

It is the subset of AI. It is the study which provides a system to learn and improve on its own through their experiences.

"Machine Leaning allows the computers to learn from the experiences by its own, use statistical methods to improve the performance and predict the output without being explicitly programmed."

The popular applications of Machine Learning are Email spam filtering, product recommendations, online fraud detection etc.

How does Machine Learning works?
The working of machine learning models can be understood by the example of identifying the image of a cat or dog. To identify this, the ML model takes images of both cat and dog as input, extracts the different features of images such as shape, height, nose, eyes, etc., applies the classification algorithm, and predict the output.
Consider the below image:

working-of-machine-learning

Deep Learning

It is the subset of AI and ML. It is the study in which machines are trained on its own by examing the algorithms and rules. It focuses on Artificial Neural Network (ANN).

It works technically in the same way as machine learning does, but with different capabilities and approaches. It is inspired by the functionality of human brain cells, which are called neurons and leads to the concept of artificial neural networks. It is also called a deep neural network or deep neural learning.

Some popular applications of deep learning are self-driving cars, language translation, natural language processing etc.

How does Deep Learning Works?
We can understand the working of deep learning with the same example of identifying cat vs dog. The deep learning model takes the images as the input and feed it directly to the algorithms without requiring any manual feature extraction step. The images pass to the different layers of the artificial neural network and predict the final output.
Consider the below image:

working-of-deep-learning

Machine Learning vs Deep Learning

The key differences between both terms based on different parameters are:

Parameter Machine Learning Deep Learning
Data Dependency Machine learning can work with a smaller amount of data. Deep Learning algorithms highly depend on a large amount of data for good performance.
Execution time Machine learning algorithm takes less time to train the model but it takes long-time duration to test the model. Deep Learning takes a long execution time to train the model, but less time to test the model.
Hardware Dependencies Since machine learning models do not need much amount of data, so they can work on low-end machines. The deep learning model needs a huge amount of data to work efficiently, so they need GPU's and hence the high-end machine.
Feature Engineering Machine learning models need a step of feature extraction by the expert, and then it proceeds further. Deep learning does not need to develop the feature extractor for each problem as it tries to learn high-level features from the data on its own.
Problem-solving approach To solve a given problem, ML model breaks the problem in sub-parts and after solving each part, produces the final result. Deep learning model takes input for a given problem and produce the end result. Hence it follows the end-to-end approach.
Type of data Machine learning models mostly require data in a structured form. Deep Learning models can work with structured and unstructured data both as they rely on the layers of the Artificial neural network.
Suitable for Machine learning models are suitable for solving simple or bit-complex problems. Deep learning models are suitable for solving complex problems.