Machine Learning and Artificial Intelligence are rapidly changing the landscape of how organizations function in the world. From data analysis to make independent decisions based upon past experiences, Machine Learning is being used to help organizations make informed decisions but before any of that happens, the algorithms and associated software have to be trained accordingly.

 

Two methods namely supervised learning and unsupervised learning, are widely used to train AI programs. Both are widely used to train software but have significant differences between them, which change the way both models function completely:

Supervised Learning:

Supervised Learning can be considered equivalent to teaching a toddler how to walk or so forth. The software will have a dataset as well as corresponding input and output pairs which can form a training model for the software. Linear Regression is an example of Supervised Learning and in this case, regression is used when the output is a real number or quantity, let’s say dollars or weights. Likewise, Classification models can be used if we want our output in different categories like ‘cancer’ or ‘no cancer’ etc.

 

Unsupervised Learning:

Unsupervised Learning consists of methods to train AI software without a training model. In the first scenario discussed above, we had input variables and some possible outputs. However in Unsupervised Learning, we only have a dataset and the software goes over it bit by bit, analyzing all the data points and then finding any suitable patterns which match the dataset. Consider you have a large database of entries from an Air Quality Monitoring system and you want to make sense of the data, which hasn’t been sorted yet, the software will go through each part and create a pattern from which you can deduce meaningful results.

 

Neural Networks work on the concept of Unsupervised Learning. Another example of Unsupervised Learning is anomaly detection. 

Semi-supervised Learning:

In semi-supervised learning, we apply a mixture of supervised and unsupervised learning techniques to make sense of the dataset. This method is used when we have a dataset that has some points labeled whereas much of the dataset currently has no meaning. Therefore, we will apply supervised learning on the labeled dataset and unsupervised learning on the unlabelled dataset. For example, you can apply Linear Regression on some parts and dedicate a neural network to understand the remaining part of the dataset.

 

Conclusion:

With those 3 techniques in view, we can deploy AI software on any sort of datasets to extract meaningful results from them. The reason why organizations can boost their revenues by deploying this software is that they make meaningful decisions based upon supporting data from the past. Therefore their future strategies as predicted by the AI being used generally help them boost their revenues.