While most of us work with machine learning or Artificial Intelligence on some level, we only have the vaguest idea of how it really works. While, there are plenty of people out there that haven’t even heard of it, most people think that machine learning is a type of software. They couldn’t be more wrong. Machine learning is a set of algorithms that allow an application to collect and interpret data so that it can accurately predict certain outcomes. The process is akin to data mining or predictive modeling. If you have ever ordered something off line, then you know that you start getting ads based on what was bought, this is machine learning. While most of us know machine learning in retrospect to things like this, it is also used to build news feeds, fraud detection, network security, spam filtering, and do predictive maintenance.
Below are the steps used in the machine learning process:
- Identification of datasets
- Preparing relevant data sets for analysis
- Selection of the proper machine learning algorithm
- Analytical model construction based on selected algorithm
- Use test data sets to train the local model
- Revised as necessary
- Make sure there is movement of the models process score and other results
Some of the known applications for machine learning in business are BI (business intelligence), HR (human resource) systems, and CRM (customer relationship management). Self-driving vehicles also utilize this process to identify objects that they encounter. It is used to determine the proper course of action so that they can maneuver the vehicles. Machine learning has always been a major part of vendors such as IBM, Amazon, Microsoft, and Googles as they race against one another to sign up clients for their machine base platforms. What we need to do as artificial intelligence is incorporated more into multiple enterprises is to continue to use the platform as it becomes more proficient.