Data Science, Artificial intelligence (AI), Machine learning (ML) & Deep learning (DL)
There seem to be clouds of misconceptions & confusion among modern technologies such as Data Science, Artificial intelligence (AI), Machine learning (ML) & Deep learning (DL) - these four terminologies are usually used interchangeably to describe software that behaves intelligently.
Data science is the study of data & aims to use a scientific approach to extract meaning and insights from data, Deep learning is a subset of machine learning, and machine learning is a subset of AI - all machine learning is AI, but not all AI is machine learning, so is there a difference between artificial intelligence, machine learning, and deep learning.
What is Data Science?
Data science is a big umbrella and rapidly growing with massive potential that harnesses the widespread amounts of data and processing power available to gain insights covering each and every aspect of data processing and not only statistical or algorithmic aspects but it involves developing methods of process manipulations that are involved in analyzing data from multiple sources, applying machine learning, predictive analytics and visualizing data that generate various insights that will serve countless business purposes and is used to tackle big data and includes data cleansing, preparation, and analyze and apply, and sentiment analysis to extract critical information to forecast the future based on past patterns. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured
Data science sheds light on areas like customer behavior, operational shortcomings, supply-chain cycles, predictive analysis and more. Data science is crucial for companies to retain their customers and stay in the market.
What is Artificial intelligence (AI)?
Artificial intelligence is an intelligence to add all the capabilities to machines that humans contain. AI is a technology that enables a machine to simulate human behavior. Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is called artificial intelligence.
AI is an automated decision-making system, which continuously learns, adapts, suggests and takes actions automatically. At the core, they require algorithms that are able to learn from their experience. This is where Machine Learning comes into the picture.
What is machine learning (ML)?
Machine Learning is a subset of artificial intelligence, It's a technique for realizing AI & method of training algorithms that they can learn how to make decisions. ML is a science of designing and applying algorithms that are able to learn things from past Data, If some behavior exists in the past, It can predict if or it can happen again. If there is no past behavior then there is no prediction. The machines learn from history to produce reliable results over large data sets, analyzing the patterns in data and facilitating machines to respond to different situations for which they have not been explicitly programmed.
The ML algorithms use Computer Science and Statistics to predict rational outputs & uses data to feed an algorithm that can understand the relationship between the input and the output. Some of the important algorithms are regression, classification and clustering techniques, decision trees and random forests and In addition to these, there are many algorithms that organizations can use to serve their unique business needs.
Machine learning is that the machine can learn without human intervention. The machine needs to find a way to learn how to solve a task given to the data.
What is Deep learning (DL)?
Deep learning mimics the network of neurons in a brain & It is a subset of ML and it's called deep learning because it makes use of deep neural networks. A neural network architecture has many layers that are stacked on top of each other and provide a different interpretation of the data. The final layer is the output layer- provides an actual value for the regression task and a probability of each class for the classification task.
Whenever we see a piece of new information, the brain tries to compare it to a known item before making sense of it, which is the same concept with deep learning algorithms to identify patterns and classify various types of information such as image recognition, sound recognition, recommender systems, natural language processing, etc.
Deep learning is a breakthrough in the field of artificial intelligence. When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation. The main reason is the feature extraction is done automatically in the different layers of the network.
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