Machine learning is everywhere. And although subtle, it makes all of life (and business) a whole lot more exciting.
Without it, we wouldn’t have Siri or Alexa. We wouldn’t receive tailor-made recommendations on Netflix or Youtube. And there would be no intricate chatbots to give us fluid conversational experiences.
The great thing is that the use cases can seem endless. Especially considering the potential it has across various industries.
So we’re going to explore what machine learning is and how it works. By the end of this, you should have at least a basic understanding of ML.
What Is Machine Learning?
Machine learning (ML) is rooted in artificial intelligence (AI). It essentially gives systems and processes the ability to learn and improve through experience. This is without the need to undergo manual or explicit programming.
According to IBM, “Machine learning is a branch of AI focused on building applications that learn from data and improve their accuracy over time without being programmed to do so”.
Essentially, it’s the development of applications that can access, learn from and use data. These applications have special programming that allows them to both learn from experience and improve their accuracy over time.
Much like we as humans do.
How Does ML Work?
ML goes through a number of unique and complex processes to have it function at its full potential.
Ultimately, it all begins with the process of learning.
In the same way that we observe data (instructions, examples, experience) to learn, find patterns and make formulated decisions, so does an ML algorithm.
An algorithm is like a sequence of steps or rules to be acted out on data.
This is with every decision or action based on static and/or dynamic data provided. Almost like a calculation or a recipe. The data that you input (a sum or ingredients) will follow certain steps to provide an output (an answer or a meal).
It essentially works by taking in various input data, following a set of rules and giving an output on the sequence that is programmed to follow.
A model is essentially the trained output of an algorithm. After running an ML algorithm on training data, the model represents the rules, numbers and algorithm procedures required to make predictions. These models consist of both data and instructions for using the data to make predictions.
With ML, it acts in the way that a human does in that it can operate without manual intervention. In pretty much the same way that you learn a recipe, route or formula by heart and use your knowledge to guide the actions, decisions or answers you make.
Types of ML Algorithms
ML is quite complex, especially considering that it’s meant to replicate the way that humans process information.
Because of its intricate nature, ML has a diverse number of ways to make it work.
The 3 main ML algorithms that exist are:
This type of ML learning trains models to make use of labeled datasets for classification or accurate outcome prediction.
All of this is supervised and corrected by a specified algorithm “taught” by the developer.
ML engineers or data scientists then apply previously learnt data with new data for higher accuracy in real-time.
There are usually two types of problems it solves, Classification and Regression:
With Classification, the aim is to predict outcomes from given samples with the output in the form of a category or class. Categories could include cat and dog, strong and weak, etc. You essentially feed the model data to categorise in the form of a class label.
Regression is based on predicting outcomes from given samples that have an output in the form of labels with real values or that are numeric. Real-value labels are those that denote things like age, height, rainfall, etc. With it, you take variables like descriptions and output numeric labels for different use cases.
Then there’s ensembling. Which is essentially the combination of multiple weak ML models to make predictions on a new sample.
This type of learning is different in that there is no corresponding output for the input data. It involves using a model to extract, describe or identify relationships in data without a clear mapped path to take. Unlike supervised learning, there is no teacher.
The two main problems that this type of learning solves is:
Association, to discover the probability of a link between items or variables in various situations or contexts. Commonly used in market-basket analysis, it allows you to determine the probability of a customer to buy an item based on them purchasing another related or unrelated item.
For example: if a person buys shavers, they are 60% more likely to buy shaving cream.
Clustering, to group, cluster or categorise data samples that have similarities closer to together. An example could be a dating app, where the model would cluster certain options for the user to find the most suitable match.
Reinforcement learning is an ML algorithm that gives an agent (ML subject) the ability to learn from behaviour and take action based on feedback (positive or negative). These algorithms learn to conduct behaviours that maximise the reward and develop on their own through trial and error.
This type of algorithm is usually popular in robotics and gaming. Where robots can learn to avoid objects and AI players can learn to improve their gaming abilities.
Moving Forward With Machine Learning
ML is a fascinating technology and it’s only getting better with time.
If you’re already a fan of ML and know what it’ll mean for your business, then click here to find out about how we operationalise your ML models for tremendous value.