Artificial Intelligence is not a thing.
It's more of an umbrella term that brings together several subfields of computer science. This field is divided into multiple parts, algorithms, theories, and applications.
Each has different goals and methods to pursue them. Some are achieving their goals better than others doing it in the same timeline or even close to it.
Machine learning is one of the subfields of Artificial Intelligence. It refers to the process of getting a computer to learn from data without being explicitly programmed.
The term machine learning was coined in 1959, but its history goes back to the pre-code era (around mid 19th century) with the discovery of Bayes' Theorem. It became popular around the 90s and should not be confused with other terms like 'Artificial Neural Network' or 'Deep Learning.'
Other fields of AI that are not machine learning.
There are siblings to machine learning under the parenthood of artificial intelligence. Some of the siblings are natural language processing, cognitive computing, robotics, and computer vision. These fields were built around different concepts than machine learning.
Natural Language Processing (NLP)
Natural language processing is teaching computers to understand and generate human languages.
It combines the rule-based modeling of human language with statistical, machine learning, and deep learning algorithms. Today, NLP is applied to many tasks such as machine translation, text summarization, and dialogue systems.
Cognitive computing is the process of making a computer system that can think like humans.
A breakthrough in this subfield is the discovery of neural networks. Neural networks are a way of simulating the workings of the human brain.
Cognitive computing is used in fields such as image and speech recognition,
Robotics is a subfield of AI that deals with the design, construction, and operation of robots. Robotics is perhaps the most mature subfield of AI and has seen significant commercial deployment.
Computer Vision means the ability of computers to interpret and understand digital images. It is mainly used in tasks such as facial recognition, object recognition, and scene understanding.
Machine learning still plays a central role in Artificial intelligence.
It serves as a subfield of AI on its own, but it's also being used in other fields.
The first known use is back in the 50s by Alan Turing, who introduced it in his Computing Machinery and Intelligence paper.
After that, researchers have been building programs on machine learning which are now critical to the success of many AI applications.
Machine learning is more widespread and has more research done on it. It's also been commercially successful in specific areas such as predictive analytics, fraud detection, and search engines.
Machine learning is not a standalone subfield, but it is a critical component to other successful subfields.
One example is in computer vision, where machine learning is used for tasks such as object recognition and scene understanding.
In natural language processing, machine learning can be used for tasks such as text classification and sentiment analysis.
Where to start learning machine learning? Machine learning has its roots deep in statistics.
For example, you can think of machine learning as a subset of predictive analytics that deals with pattern recognition and decision making.
So if you have some knowledge of statistics, it will be easier to understand the concepts behind machine learning.
It's also helpful to have programming skills since a lot of machine learning is done through coding.
But wait, even if your statistical knowledge and programming skills aren't very good, you can still become a machine learning engineer.
Visual analytics tools such as KNIME and RapidMiner make it easy for you to learn and use machine learning without having to code.
These tools provide a graphical interface where you can drag-and-drop algorithms and connectors to create data pipelines.
You don't need any coding skills to do this, and you can get started in minutes.
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