What is the difference between Machine Learning and Deep Learning?

I learned some time ago about artificial intelligence. The machine learning and deep learning are subsets of artificial intelligence which has attracted a lot of attention by programmers. The machine learning is an artificial intelligence that involves the creation of algorithms that is capable of modifying itself without the intervention of any human to produce the desired and intended output via feeding itself through structured data.

The deep learning, on the other hand, is a subset of machine learning in which algorithms are created to function in the same light as machine learning, but there are different layers of these algorithms, each providing different interpretations as regards the data it feeds on. This network of the algorithm is called an artificial neural network, named as an attempt to imitate the function of the human neural network in the brain.

Artificial Intelligence (AI) and Machine Learning (ML) are two words casually thrown around in everyday conversations, be it at offices, institutes, or technology meetups. Artificial Intelligence is said to be the future enabled by Machine Learning.

Now, Artificial Intelligence is defined as“the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”Putting it means making machines smarter to replicate human tasks, and Machine Learning is the technique (using available data) to make this possible.

Some have assumed that deep learning is just the same with machine learning but these two types of learning have their own differences. Machine learning has to make use of the algorithm in order to acquire data. Once the data is acquired, there is a need to learn about the data further before making the right decisions based on the data.

For deep learning, there is a need to create networks that will allow the network to make intelligent decisions on its own. The performance of the machines as they continue learning will differ depending on the type of learning that is being done.