Machine learning is all the rave now and there are so many Machine Learning Applications and uses. It is quite exciting really, new technology always is. With all the capability that machine learning processes, it is no surprise that many industries have found ways to apply it to their processes and greatly improve different areas. Below we discuss the various ways machine learning has been applied.
Machine Learning Applications: Market Basket Analysis
Market basket analysis is a data modeling technique based upon a theory that if you buy a certain group of items you are more likely to buy another group of items. Machine learning is used by retailers to understand the purchase behavior of customers and predict their interests. It then goes ahead and makes suggestions to most of which are always accurate and this leads to increased sales.
It is also used to improve the store layout because the machine learning maps the flow of customers and this is used to optimize product placement and improve the shopping experience. Machine learning, through data mining, aids in aligning customers into a distinct segment and then the company can tailor their offerings according to their customers’ needs, geographic location, or demographic.
Data mining allows finding a segment of customers based on vulnerability or location specific needs and the company could create special offers and enhance satisfaction or create special marketing strategies that speak to the needs of the customers.
Machine Learning Applications: Education
I find the emergence of advanced technology with education the most exciting. Machine learning is being used to improve the giving and receiving of education through software programs like educational data mining and assisted teaching assistants.
With educational data mining, school systems can predict the future learning behavior of students and curate a curriculum to suit it. This helps the educational institution decided what to teach and how to teach it. It captures and tracks the learning pattern of students and this information can be used to develop more advanced teaching techniques.
Machine Learning Applications: Customer Relationship Management (CRM)
A Customer Relationship Management system which is also referred to as a CRM is used to interact with customers. It covers everything from acquiring customers, customer service, to retaining customers. This system helps organizations implement customer-focused strategies that would help the company keep them happy and retain their business or patronage. With data mining technologies the collected data can be used for analysis. Instead of being confused where to focus to retain a customer, the data tells the organization where to focus their energies. This data can also be used for target marketing campaigns. can tailor their products and marketing strategies to fit different customer segments in different locations or at different buying seasons. This data can also be sold to other companies looking for market research or benchmarking data.
Machine Learning Applications: Fraud Detection
Fraud detection is a huge concern for many industries, especially the financial industry. With machine learning, financial organizations can use data to recognize meaningful patterns that could lead to early fraud detection. The data also helps detect anomalies in systems, faux trends, errors, and fraudulent activities. With the mined data, a model can be built and then this model is used to identify when there is an anomaly or in more advanced versions when there is fraud or the semblance of it.
Machine Learning Applications: Intrusion Detection
Just as machine learning can be used to detect fraud, it can also be used to detect intrusions into organizations systems. Intrusion can take many forms but the most common is hacking. If a company is being hacked or if someone is trying to hack into their system by breaking down their firewall, the machine learning detection system sends out an alert and even uses inbuilt mechanisms to stop it. The level of focus on each kind of detection can be specified and a system can be built that sends out different alert based on the intensity of the attack or threat of intrusion. The data mining capabilities of machine learning are also used to extract data after the attack so it can be studied and even reversed engineered in order to trace the source.
Machine Learning Applications: Lie Detection
Not many people have taken a lie detection test but they are getting more and more common. The lie detection capabilities of machine learning are used mostly by law enforcement agencies. It can be used to investigate crimes, interrogate suspects and even monitor suspects using hidden devices. When enough data is collected, a model can be built an that model can be applied to cases and similar criminal occurrences or investigation. As more cases are worked on, more data is collected and the system is improved on and it becomes more and more accurate.