Understanding Artificial Intelligence, Machine Learning and Data Science

The three fields, Artificial Intelligence, Machine Learning and Data Science are often misunderstood by people and used interchangeably. A Data Scientist looks at the data from various angles. Thus, Data Science is mainly used for making predictions and decisions with the use of prescriptive analytics, predictive causal analytics, and machine learning. Even though all these three fields are related, they refer to three very independent branches of research. Here we explain each one of them in detail.

photo/ Gerd Altmann via pixabay

Artificial Intelligence

Artificial Intelligence refers to branch of science which simulates the thinking and decision making process of the human brain. It does it by creating artificial neural networks, in a manner analogous to how the human brain functions. Artificial Intelligence, if developed properly, has applications in almost every walk of life. However, machines are inherently dumb, and to make them act intelligently requires some very complicated programming and computing resources.

Artificial intelligence is broadly categorized in two parts: (i) General Artificial Intelligence: This refers to a machines capability of making decisions in a wide range of situations; and (ii) Narrow Artificial Intelligence: This refers to a machines capability of handling one particular situation with great accuracy. For example, a general AI machine should be able to play any board game with equal ease, while a narrow AI machine will play only one board game with great efficiency. Most of the AI in action today is the narrow AI. General AI is still a farfetched human dream. You can also go for data analytics certification which is very beneficial for

organizing huge amount of data. The

Machine Learning

Machine learning is the ability of a computer program to make it smarter over time by learning from its surroundings. A good artificial intelligence algorithm does not need to be coded again and again and it enables the machine to become smarter over time by experience. AI can also be used for doing predictive analysis.

Most modern day data science approaches can be broadly classified in three basic models: (I) Supervised: Here the data is given a sample set and told explicitly what the information stands for. Using this information, the data can make an intelligent guess about the nature of the future data fed to the machine. (ii) Unsupervised: Unsupervised mechanical learning implies the methodology where the machine is fed a whole bunch of information, and the mechanical classifies the data itself based on the inherent logics coded in the algorithm; and (iii) Reinforcement: here the machine learns by interacting with the environment and then analyzing its actions based on the feedback given by the environment. For example, if the algorithm plays a game of chess, it will analyze the whole game based on the outcome of the game.

Data Sciences

Data science course is simply the extraction of relevant information from a given amount of information. Data science is used extensively in fields of science like computer programming, statistics and pattern recognition. Data science deals with both small datasets and huge databases.

Data scientists often use machine learning to make predictions about the future based on the available data. Data science can be looked at as a practical application of machine learning with emphasis on analyzing and solving real life problems.

Author: Lynn Joseph