Artificial Intelligence (AI) relies on various technologies and one of them is machine learning. Machine learning is ongoing data analysis that allows the system to refine its operations in response to new information.
Computers that use machine learning employ algorithms autonomously to learn from information sets iteratively. Machine learning has existed as a concept for decades, but the facility to automate algorithms to assess large amounts of data has only, but fairly recently. Now that this particular genie is out of the bottle it’s growing greatly in popularity because the more we look, the more opportunities we seek to employ it productively.
From an overview perspective, machine learning is the ability to iteratively and independently adapt to new data. Apps use pattern recognition to learn from previous results, so in that way they can mimic one aspect of human behavior.
How Does Machine Learning Work?
Machine learning attracts a lot of interest, no doubt because the idea of a computer learning for itself is intriguing. It hints at the possibility of self-awareness, and even though this isn’t yet possible (and may never be) machine learning still deserves our interest.
The process begins when training data is entered into the chosen algorithm. Training data can be known or unknown information and it serves to shape the final machine learning algorithm.
The machine receives learning algorithm test data to check that it is functioning properly. Predicted and actual results are compared to determine this. If too don’t align then the algorithm will be trained again and again until the point when the required outcome is reached. This is how the machine learning algorithm is able to learn continuously and refine its level of accuracy.
What are the Different Types of Machine Learning?
Machine learning is divided into two main fields: supervised and unsupervised learning. Around 70% of machine learning is supervised and 10-20% is unsupervised. Reinforcement learning accounts for the remainder.
1. Supervised Learning
This involves training the algorithm using known or labeled data. The fact that the data has been labeled and is therefore known is why we refer to it as ‘supervised’. It’s a guided process that helps to educate the model.
After the model has been educated using the known data, unknown data can be fed in, and this will obviously produce a different response. For example, once it has been trained to recognize an image of an orange, images of other fruit can be fed in to refine its accuracy.
2. Unsupervised Learning
This involves feeding the algorithm with unknown and unlabeled training data. As it is unknown the algorithm receives no guidance on it, which is why this approach is termed unsupervised. As the machine learning algorithm trains on the data it searches for patterns and attempts to respond correctly. This may be difficult of course. If we look at the example of oranges again, introducing pictures of nectarines, which sometimes look similar can be challenging for the system, but the more it practices the better it gets at recognizing them.
3. Reinforcement Learning
With this approach, the algorithm discovers data using trial and error and then decides what actions give the highest rewards. Reinforcement learning consists of: the agent, the environment, and the actions, the agent being the decision-maker learner, the environment referring to what the agent interacts with, and the actions being what the agent does.
Reinforcement learning happens when the agent selects actions that boost the anticipated reward over a set period. This is achieved most easily when the agent works inside a framework of side policies.
Why is Machine Learning Important?
Self-driving cars, novel writing software, online security, and predictive text all make use of machine learning. It helps to bridge the gap between computers and people, and it does this by quickly analyzing huge amounts of information and using it to make decisions. Computers may not be alive, but we are able to train them to the point where their actions become indistinguishable from those of humans. The technology has become available at a time in human history when the volume of data on pretty much everything is abundant and available (privacy concerns aside). Almost all of it can be used to train machine learning algorithms to perform functions that we didn’t even know we needed.
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