What is machine learning? Definition and examples
Machine learning is an artificial intelligence application that gives ‘smart’ machines the ability to learn and improve automatically. They improve from experience, even though computer scientists had not programmed them explicitly for certain tasks.
It is also the scientific study of statistical models and algorithms that machines use to carry out a task effectively without receiving explicit instructions. They rely on inference and patterns instead.
The term is all about developing software technology that lets machines access data and then use it to learn by themselves. It’s important to note that while machines can adjust and improve based on data without a human explicitly programming each adjustment, the initial setup, choice of algorithm, and provision of data are all done by humans.
Humans have the ability to learn by experience. Machines with artificial intelligence can do the same.
Emerj.com has the following definition of the term:
“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
A neural network – a set of algorithms that has been modeled after the human brain, is an example of machine learning.
AI and Machine Learning
Think of artificial intelligence (AI) as the brain of a robot. Just as our brains help us think, make decisions, and understand the world, AI gives machines a kind of “brain” to do similar things. One of the most powerful tools this “brain” uses is called machine learning.
Imagine you’re teaching a young child to recognize different fruits. At first, the child might not know an apple from a pear. But by showing them many pictures and correcting their mistakes, they learn. That’s a lot like how machine learning works. Instead of programming a computer to know every single fruit, we feed it lots of data (like pictures) and let it figure out the patterns.
So, while AI is like the entire brain, machine learning is like the part of the brain that learns from experience. Most of the advanced things machines do today, from understanding voice commands to recommending movies, come from this learning part.
In short, AI provides the “thinking power” for machines, and machine learning teaches them how to use that power effectively.
Human brains are naturally adept at recognizing patterns. Take, for instance, a simple mathematical quiz:
- 2 – 20
- 5 – 50
- 10 – 100
- 100 – 1,000
- 500 – ?
From this sequence, many individuals would quickly deduce that each number on the left is multiplied by 10 to yield the number on the right, leading to the conclusion that the answer for 500 is 5,000.
But how do machines, specifically machine learning algorithms, handle such tasks?
- Data Input: In a machine learning context, this pattern would be input as training data. The algorithm would see a set of inputs (2, 5, 10, 100) and their corresponding outputs (20, 50, 100, 1,000).
- Model Training: A machine learning model would then be exposed to this data, trying to find a relationship (in this case, a multiplication factor) that maps the inputs to the outputs. Note that the model doesn’t “understand” multiplication as humans do – it’s looking for patterns and relationships in the data it’s provided.
- Prediction: Once the model identifies the pattern, it can predict the output for a new, unseen input. For the input 500, a well-trained model would predict the output as 5,000, mirroring the human deduction.
- Iterative Learning: Unlike the straightforward example given, real-world data can be noisy and filled with exceptions. Machine learning thrives on such complexities. As more data gets fed into the system, the model refines its internal parameters to improve accuracy. This is akin to a human learning from more examples and experiences.
- Complex Patterns: This example uses a simple linear relationship. However, machine learning models can capture intricate, non-linear relationships. For instance, if the pattern involved a combination of multiplication, addition, and even more complex operations, machine learning models like neural networks are designed to navigate such complexities.
- Generalization: One of the critical challenges in machine learning is ensuring that the model doesn’t just “memorize” the training data but generalizes patterns to make accurate predictions on data it hasn’t seen before. For our example, if we only trained our model with the input 2 and its corresponding output 20, and then asked it about the input 500, it would need to generalize the learned pattern rather than recall from specific examples.
Put simply, machine learning aims to encode the kind of pattern recognition and extrapolation humans naturally perform into algorithms that can scale and adapt to vast and complex datasets. While the underlying mathematics and principles might be intricate, the goal remains: teaching machines to learn from experience and make predictions or decisions based on identified patterns.
A team process
According to American multinational IBM, machine learning allows us to learn continually from data. This helps us, for example, to predict the future.
Powerful sets of models and algorithms are being used across most industries. They improve processes and help us gain insights into patterns and anomalies within data.
However, it is not a solitary endeavor. It is a team process that requires business analysts, data scientists, and data engineers. It also requires business leaders. They all work together.
“The power of machine learning requires a collaboration, so the focus is on solving business problems.”
The term ‘Machine Learning’ was first used by Arthur Samuel (1901-1990) in 1959. Samuel, from Emporia in Kansas, USA, was a pioneer in AI and computer gaming.
He was among the first computer experts to realize that rather than teaching machines everything they need to know, they could be taught to learn for themselves, just like we do.
Arthur Samuel once said:
“Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.”
These five terms are closely related to ‘Machine Learning’ and ‘Artificial Intelligence.’
- Deep Learning: Machine learning with advanced neural networks.
- Natural Language Processing (NLP): AI that understands and generates human language.
- Computer Vision: AI that interprets images and video.
- Robotics: Design and use of robots, sometimes with AI.
- Cognitive Computing: Systems simulating human thought.
Video – What is Machine Learning?
In this educational video, from our sister channel on YouTube – Marketing Business Network, we explain what ‘Machine Learning’ is, using simple and easy-to-understand language and examples.