What is Data Science, Artificial Intelligence and Machine Learning?

Now a days everyone talk about this fancy words DS, AI and ML.

So, in this blog we will look in details what exactly this terms means.

Data Science: Data science is the extraction of relevant insights from sets of data. It uses various techniques from many fields like mathematics, machine learning, computer programming, statistical modeling, data engineering and visualization, pattern recognition and learning, uncertainty modeling, data warehousing, and cloud computing. Data Science does not necessarily involve big data, but the fact that data is scaling up makes big data an important aspect of data science.

Data science is the most widely used data driven technique among AI, ML and itself. The practitioners of data science are usually skilled in mathematics, statistics, and programming (although expertise in all three is not required). Data scientists solve complex data problems to bring out patterns in data, insights and correlation relevant to a business.

Artificial Intelligence (AI): Artificial intelligence refers to the simulation of a human brain function by machines. This is achieved by creating an artificial neural network that can show human intelligence. The primary human functions that an AI machine performs include logical reasoning, learning and self-correction. Artificial intelligence is a wide field with many applications but it also one of the most complicated technology to work on. Machines inherently are not smart and to make them so, we need a lot of computing power and data to empower them to simulate human thinking.

Artificial Intelligence (AI): Artificial intelligence refers to the simulation of a human brain function by machines. This is achieved by creating an artificial neural network that can show human intelligence. The primary human functions that an AI machine performs include logical reasoning, learning and self-correction. Artificial intelligence is a wide field with many applications but it also one of the most complicated technology to work on. Machines inherently are not smart and to make them so, we need a lot of computing power and data to empower them to simulate human thinking.

Artificial intelligence is classified into two parts, general Artificial Intelligence and Narrow Artificial Intelligence. General AI refers to making machines intelligent in a wide array of activities that involve thinking and reasoning. Narrow AI, on the other hand, involves the use of artificial intelligence for a very specific task. For instance, general AI would mean an algorithm that is capable of playing all kinds of board game while narrow AI will limit the range of machine capabilities to a specific game like chess or scrabble. Currently, only narrow AI is within the reach of developers and researchers. General AI is just a dream of researchers and perception among the masses that will take a lot of time for the human race to achieve (if ever possible).

Machine Learning (ML): Machine Learning is the ability of a computer system to learn from the environment and improve itself from experience without the need for any explicit programming. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered. Machine learning can be performed using multiple approaches. The three basic models of machine learning are supervised, unsupervised and reinforcement learning.

In case of supervised learning, labeled data is used to help machines recognize characteristics and use them for future data. For instance, if you want to classify pictures of cats and dogs then you can feed the data of a few labeled pictures and then the machine will classify all the remaining pictures for you. On the other hand, in unsupervised learning, we simply put unlabeled data and let machine understand the characteristics and classify it. Reinforcement machine learning algorithms interact with the environment by producing actions and then analyze errors or rewards. For example, to understand a game of chess an ML algorithm will not analyze individual moves but will study the game as a whole.