Artificial Intelligence, Machine Learning, Deep Learning, and Data Science Explained
You may have heard of the terms artificial intelligence, machine learning, deep learning, and data science in today's technologically advanced world. Though they seem impressive, what do they actually mean?
In upcoming posts we will be exploring Artificial Intelligence in an accessible, beginner-friendly and less technical way with the aim of making Artificial Intelligence easy to understand for all
What is Artificial Intelligence?
Think of Artificial Intelligence as a whole, under which Machine Learning and Deep Learning falls. An artificial intelligence application can complete its tasks without the assistance of a human. Machines can understand, learn from experience, and adapt thanks to artificial intelligence (AI). The application of AI can be seen in self-driving cars, chess computers, Netflix suggestions, and more.
AI can perform tasks that normally requires human intelligence. It can learn, solve problems and even take decisions.
There are some tasks at which AI can even outperform us humans. AI is better than humans at pattern recognition, playing board games like Chess and Go, at handling repetitive data, at completing data-driven tasks and making data-driven decisions.
So basically, Artificial Intelligence is the ability of a computer or other machine to perform those activities that are normally thought to require intelligence.
What is Machine Learning?
Machine Learning is the subset of AI (as you can see in the above diagram). Machine Learning (ML) is like the learning process for AI systems. Instead of explicitly programming the computer to do a certain task, you give the computer some data and algorithms that allow it to learn from that. ML algorithms improve their accuracy when they're provided with more data and repeatedly learning from the given data over time (iterations), making predictions and decisions based on the patterns they've recognized in the data.
According to the IBM: Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
What is Deep Learning?
Deep Learning is a subset of Machine Learning which focuses on neural networks. Think of Deep Learning (DL) like a complex system of artificial neurons (hence neural network) which try to mimic how the human brain works. Deep Learning processes data in a way that is inspired by how the human brain works and how it is structured. The neural networks consist of interconnected nodes (neurons) that processes information, much like how our brain works.
Neurons are the cells in the brain which are responsible for our brain's functioning.
Deep learning has shown extraordinary results in tasks like image and speech recognition, as it can automatically learn intricate patterns from a vast amount of data.
What is Data Science?
Data science is the study of data to extract meaningful insights for business and research purposes. It is a multidisciplinary field that combines domain expertise, programming skills and statistical knowledge to gain insights and knowledge from the data. Data scientists use various techniques like Artificial Intelligence, Machine Learning, and Deep Learning to analyze and interpret complex data sets, enabling businesses to make informed decisions.
|Artificial Intelligence (AI)
|Computer systems that mimic human intelligence, learn from experience, and perform tasks autonomously. Examples: self-driving cars, chess computers, Netflix suggestions.
|Machine Learning (ML)
|Subset of AI where computers learn from data and improve performance over time. Imitates human learning.
|Deep Learning (DL)
|Subset of ML using neural networks to process data, inspired by the human brain. Great for complex tasks like image and speech recognition.
|Interdisciplinary study of data to gain insights for business and research. Uses AI, ML, and DL techniques to analyze complex data sets.
Are you new to the AI world? Feel free to ask questions about the concepts discussed in the post in the comments.