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Machine Learning: How to use machine learning algorithms to supercharge your business

Head outline in blue with circuits
(Image credit: Gerd Altmann from Pixabay)

Machine Learning (ML) has become an important aspect of modern business and research since the term was first coined in 1952 by computer scientist Arthur Samuels. Based on Donald Hebb’s 1949 model of brain interaction, in the past 70 years the technology has gone from a computer beating a human at a game of checkers to something that is part of our daily lives. ML helps social media sites run, for example by automatically tagging photos on Facebook, and has even helped to make self-driving vehicles a reality.  

In business, ML has also been responsible for some of today’s most significant advancements in technology, such as the development of medical imaging, fraud detection, chatbots and much more. Use cases like these are helping organizations to create more personalized experiences for their customers, as well as detecting fraud and better analyzing data. Furthermore, as the technology continues to learn and evolve it will only become more essential for business growth and development. From Supervised Learning that uses labeled training data (for example in Identity Fraud Detection), to Unsupervised Learning which uses clusters of data for customer segmentation, to Reinforcement Learning which is the newest form of ML – often used in the games industry and real-time decision making - businesses shouldn’t shy away from using this game-changing technology because the impact it can have is enormous. 

In this article I will provide some context around what ML is and how it differentiates from Artificial Intelligence, or AI, before discussing the three types of ML and how these can be applied to a business. I will conclude with a short prediction about the future of ML.

Machine learning vs artificial intelligence 

ML is a part of the computer science discipline and an arm of AI which, using algorithms and data, helps to assist computer systems by imitating the way that humans learn. Today ML is a fundamental element of data science that is gaining traction within businesses as a tool to drive decision making, impacting key growth metrics and providing organizations with the data to answer the most pressing questions.  

Up until the 1980s, ML was used as a training program for AI before breaking off to create a separate field of research. Unlike AI which focuses on using logical, knowledge-based approaches, ML uses algorithms and neural network models to aid computing systems in continuously improving their performance. These algorithms automatically build models that are able to make decisions without having to be specifically programmed to do so. There is often confusion around the terms AI and ML with both being used interchangeably and although ML is a subset of AI they have two very different purposes. To put it simply, AI is the larger concept of computers and machines being able to simulate human behavior and carry out tasks in a ‘smart’ way, while ML is the application of AI allowing machines to learn for themselves through access to data.  

There are many different use cases of ML today and, as touched upon, it has become an integral part of our everyday lives. Aside from the cases I referred to earlier, ML has given us voice assistants, product recommendations (think Netflix’s recommendation section), email filtering and speech recognition. It is also within businesses that we are witnessing some of ML’s biggest impacts.  

Machine learning and its business applications  

There are numerous ways that ML is currently being used to improve business performance through Supervised, Unsupervised and Reinforcement Learning algorithms. However, businesses need to firstly understand what they want to achieve with ML and what business problem the rollout of ML can help to solve. If used properly ML becomes more than just interesting tech that can provide better personalization with the organization driving the parameters of an ML model, not the other way around. Let’s look at the different use cases of each type and the types of business problems these can help to solve.  

Supervised ML 

Supervised ML, as the name suggests, is when algorithms are trained through direct human supervision where an individual can select information to present to an algorithm to determine the desired result. The algorithm will then create different processes and rules to determine an output. As the training progresses, the individual can then choose the model that best predicts the preferred output. As well as being used to predict future trends in price, sales, and stock trading - and even weather predictions - this type of ML has opened up a new layer of security and fraud protection for businesses.  

In the security space, one of the best-known applications of Supervised Learning is in BioInformatics and Speech Detection. BioInformatics is the storage of biological information such as fingerprints and facial features for face recognition, while speech recognition is when an algorithm is taught to recognize a voice. These authentication processes are used in businesses as an added layer of security to prevent and flag any attempts at fraud within the company and for their customers. Through these types of algorithms, private identity documents, such as passports, can be scanned and cross-verified against secure databases in real-time which ensures attempts at identity theft do not go undetected. Supervised Learning can also be used to detect email spam, detecting continued patterns and recognizing these as fraudulent.

Unsupervised ML 

So how do Supervised and Unsupervised Learning differ? Unlike Supervised Learning, Unsupervised Learning uses new data, not known information, to train algorithms to extract insights that can be invaluable to a business. This type of learning is used by businesses to improve efficiency and is most commonly used for digital marketing and advertising. Its ability to quickly analyze large data sets and categorize the information according to similarities and differences make it ideal for data analysis which can be applied to learn, for example, about organizations’ customer personas. For instance, Unsupervised Learning can detect customer purchasing habits allowing a company to more clearly identify buyer personas and better align their product marketing and messaging. Like Supervised Learning, this application can also be used to help prevent fraud through data similarities and differences, such as detecting unusual credit card activity and flagging as potential fraud to banks.  

Reinforcement ML 

Reinforcement Learning is the newest form of ML. Similar to Supervised Learning it develops different processes to provide a series of outcomes but in place of using existing data to learn, the model learns through trial and error. When the algorithm is able to perform a desired task, signals are sent to alert the machine and once accomplished this will then determine the next course of action. This type of learning, as previously mentioned, has most commonly been used in the games industry and more recently has helped to make self-driving cars a reality. However there are many use cases for Reinforcement Learning such as within sales teams, powering recommendation settings that can drive up consumer purchases and sales. It has also been used in many businesses to power learning-based robots to perform various tasks which can improve efficiency, cutting costs and saving time as well as having these robots perform tasks that may be too dangerous for humans to do.  

In conclusion, Machine Learning has become a very important tool for many businesses who may be using and benefiting from it without even realizing. Revealing and identifying patterns hidden in large amounts of data can provide invaluable insights which can be used to influence and provide solutions for many business decisions. As ML continues to evolve it will only become more tailored, continuing to transform businesses of all types, improving efficiency and changing the way we work. We have barely scratched the surface of what this tech can do and I look forward to seeing the full capabilities unfold.

Sean Rafter, business analysis, Firebrand Training

Sean Rafter is a subject matter expert of data & business analysis at Firebrand Training.