Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are terms often used interchangeably. While they are related to each other, let us understand how they differ. Also commonly seen in conjunction, are the terms Neural Networks (NN) and Data Science(DS). Let's look into the connections between them.
š AI is a vast field with ML, DL, and NN as its subsets. Data Science, on the other hand, shares a symbiotic relationship with AI.
Artificial Intelligence (AI) encompasses all computer systems that can perform tasks that require human like intelligence such as problem-solving, pattern recognition, language understanding, and decision-making.
Machine Learning (ML) is a subset of AI that uses statistical algorithms to identify patterns in structured data. It is the computers ability to learn from data and improve its performance over time without explicit programming. It works best with small and moderate data sets, is quicker to train
and requires less computational power. Some uses are personalised recommendations, fraud detection etc.
Deep Learning (DL) is the evolution of machine learning and neural networks. It uses advanced programming by using deep neural networks that mimic the human brain to solve complex problems by identifying patterns in large data sets. It can work with unstructured data such as images, audio files. It is harder to build and requires higher computational power. Some uses are Natural Language Processing and Image Recognition.
Data Science (DS) employs statistical and computational techniques to extract insights and meaning from data. It plays crucial role in collecting, cleaning, and preparing data for AI models. On the other hand, DS used AI to analyse data and make predictions making the relationship symbiotic.
To sum it up, ML, DL, NN are all evolution and subsets of the broader concept of AI.
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