So far we have discussed the concept of AI, its history and applications. Now, it’s time to pay attention to the three main differences between Artificial Intelligence vs Machine Learning Vs Deep Learning that are vital for the understanding of AI as well as they are linked with each other on multiple levels.

Let’s categorize AI into three sub levels:

• Artificial Intelligence
• Machine Learning
• Deep Learning

You can consider Machine Learning and Deep Learning to be the subsets of Artificial Intelligence since they both are different ways to perform Artificial Intelligence.

AI vs ML : Machine Learning

The main focus of AI or a sub-level of it is Machine Learning which is the most dynamic approach so far in current times, as it encourages machines to learn from algorithms (through data, statistical information) to do tasks without doing much programming. So, this saves time and you don’t have to tell the computer each time to perform a specific task because the computer now, would do it itself.

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What makes Machine Learning so interesting is the fact that it can not only learn from the algorithms that you provide; in fact, it can generate new algorithms from the data that you have given as the previous algorithm. So think of Machine Learning as a source of creating multiple models based that are branched out through learning the initial model.

Different Types of Machines learning

So far we have learned that machine learning is all about machine learning from the provided algorithms, however the techniques or the methods of how machine learning works can be further categorized:

Supervised Learning: Let’s say that you have a set of data which is labeled with animals specifically as cats. Now the algorithm in action looking for pictures will go after labeled information about cats only.

Unsupervised Learning: It is the opposite of supervised learning as the algorithm here will be unlabelled. It will work solely on its own discovery with the flow. Let’s say you want to purchase an iphone from amazon, so this type of algorithm will recommend you phones based on the functionality and specifications similar to an iphone.

Reinforcement Learning: This type of learning is mostly applied in real world scenarios making decisions based on learning from the environment. For example, a self driving car would make a decision based on the rationality of driving at a fine speed and keep a track of pedestrians on the side using the rational approach.

Deep Learning

Deep learning is a subset of machine learning where the learning method is based on the data representation and not the algorithms. In short, deep learning is always about learning naturally like humans learn.

The main difference between Machine Learning and Deep Learning is that Machine learning is based on performing a specific task or specific set of tasks, whereas deep learning includes neural networks that can generate results even more accurate and efficient than a human brain is capable of.

References: 

1. Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences:
   https://towardsdatascience.com/clearing-the-confusion-ai-vs-machine-learning-vs-deep-learning-differences-fce69b21d5eb

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