Machine Learning Introduction
Traditionally, humans have had to explicitly program computer inputs to generate specific responses. This is how many programs still work today. For example if you click “back” on your internet browser, it will revert you to the previous page. There are no decisions made by the computer itself, only the action of finding the previous URL and submitting it.
Machine learning takes this simple, rule-based programming to the next level, by advancing the technology to actually make decisions based on data and/or past experiences. In essence, machines are able to learn just as a human would. As more learning occurs, machines can make decisions that are more accurate and more beneficial to the user. For example, video streaming programs are able to decide what the user is most likely interested in based on their previous experiences and based on the data of previous similar users. Without machine learning, these streaming services would simply recommend whichever video it had been programmed to recommend.
What is Machine Learning?
Machine learning is a subset of artificial intelligence. It is made up of a set of algorithms learned from data and/or experiences, rather than being specifically programmed solely through business rules. Each task requires a different set of algorithms, and these algorithms detect patterns to perform certain, defined tasks.
“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co.
Machine learning is composed of algorithms whose performance improves as they are exposed to more data over time, meaning that models improve through use. Theoretically, the longer a machine learning model is used and the more data that it is exposed to, the “smarter” it will
Where is Machine Learning Used?
Machine learning is the power behind many types of automated tasks that span across various industries and use cases. Just like human learning can be applied almost anywhere, machine learning can, too. Below are a few examples of machine learning in popular applications:
- In financial trading, machine learning is used to allow for a selection of investments
- In the travel and hospitality industry, machine learning can be used for price changes based on peak travel times
- For customer service, machine learning can help power Conversational AI applications to better understand customers and help them as a live agent would
- On music and video streaming platforms, machine learning can predict what the user will most likely want to view
And this is only to name a few. Machine learning is quickly becoming a competitive advantage for businesses and is constantly being applied to new use-cases.
How Does Machine Learning Work?
Due to its vast applications, machine learning can range from being very simple to very complex. In the simplest definition, machine learning works by taking an input, such as a feature, attribute, or predictable variable, and processing it with previous data, interactions, and experiences through different algorithms, models, and techniques, and then generating an output, either through a response, class, target, or dependent variable.
The algorithms, techniques, and models that are used by the machine are programmed and tuned by humans, and therefore the scope of learning is only limited to that with which it is programmed.
What are the Types of Machine Learning?
Machine learning can take on many different forms, but it is often grouped by learning style or function. Below are the different types and their definitions:
- Supervised machine learning involves training the model explicitly and is used for more predictive analysis, such as fraud detection or image classification.
- Unsupervised learning is not trained via feedback and labeling, but the model is able to find a hidden structure in data. Examples include facial recognition.
- Semi-supervised learning; Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.
- Reinforced learning involves the machine learning model exploring its environments for decision making and learning based on a reward system. An example is the model learning how to best solve a problem based on trial and error which is a frequent use case in the finance sector.
Classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
Regression can be defined as a method or an algorithm in machine learning that models a target value based on independent predictors. It is essentially a statistical tool used in finding out the relationship between a dependent variable and an independent variable.
Decision trees are a type of supervised machine learning you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter.
Clustering is a machine learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group.
Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own.
How Can Machine Learning Benefit a Company?
There are several ways that machine learning can benefit a company. As mentioned earlier, the use-cases of machine learning are vast and varied. New ways to use machine learning are constantly being discovered. At its core, machine learning allows for companies to use data in a beneficial way to be more efficient and accurate across a broad range of functions.
At Interactions, machine learning is used to advance our virtual assistants powered by Conversational AI. With more customer interactions, the machine learning models that power our IVAs are exposed to more data and can better assist customers by delivering exceptional customer experiences.