Artificial Intelligence is a branch of computer science. It involves building smart machines capable of performing tasks that need human intelligence.
These systems mimic human cognitive functions, allowing decision making, and helping improve learning. AI-enabled systems also forecast financial and business outcomes and offer solutions for businesses. These machines interpret data to achieve business goals with greater precision and efficiency.
Machine Learning is the subsection of artificial intelligence and is based on the usage of algorithms and data. Machine learning requires complex math and a lot of coding to achieve the desired results.
Machine learning algorithms use computational methods and learn from the data without depending on any predetermined equation. It is an application of AI that allows systems to learn and improve from experience.
Deep learning is a subfield of artificial intelligence. It uses a multi-layered structure of algorithms called a neural network. DL algorithms also need data to learn and solve problems. We can also call it a subfield of machine learning.
The deep learning approach is suited to complex tasks where all aspects of the objects can’t be classified in advance. The DL system finds appropriate differentiators in the data without any external classification. Thus, there is no need for a developer to intervene.
Data science is a discipline that uses various technologies and methods to analyze data. It is the conjunction of computational science, statistics, mathematics, and knowledge of the company or business.
The main goal of data science is to find patterns in data. It uses various statistical techniques to analyze and extract information from the data. Through these valuable insights, data scientists can help companies make smarter business decisions.
Difference Between Artificial Intelligence, Machine Learning, Deep Learning and Data Science
AI ML DL DS
Uses decision trees, logic, and statistical data to mimic human intelligence
Uses statistical methods and algorithms to enable machines to learn from experience
Relies on algorithms and artificial neural networks, designed to imitate how humans think and learn
Uses math, programming, and business analysis to produce insights from huge volumes of data
Requires higher computational power to make machines acquire human-level intelligence
Requires high-performance computers with good-quality GPU for the machine learning algorithms to function
Deep learning is computationally expensive given it is remarkable at modeling diverse phenomena
Requires higher RAM to find and extract patterns in data
Chooses algorithms basis complexity of the problem, contributing towards cost and time saving
Divides a given problem into subsets, solves individually, and gives a combined output
Analyzes difficult problems in its hidden layers and performs automatic feature extraction
Collect, analyze, and define data. DS uses analytics based approaches to extract knowledge and actionable insights
Requires a lot of data to work on and obtain results
The more data a system receives, the more it learns to function better
Powered by massive amounts of data
Heavily dependent on data
It is a broader segment and focuses mainly on intelligent behavior than accuracy
Results are largely dependent on accuracy and patterns
Derives specific features from the raw input
Proposes insightful solutions from raw undefined data for effective decision making
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