When you ask Alexa to play your favorite music station on Amazon Echo, she will go to the station you played most often. As you input more data into Alexa, the algorithms will improve the delivered results. So, in order to refine your listening experience, you just have to tell Alexa to skip songs or adjust the volume. Machine Learning (ML)  and the rapid advance of Artificial Intelligence makes this all possible. And Alexa is just an easy exemple. This is an exciting branch of Artificial Intelligence, and it’s all around us. 

Nowadays, machine learning brings out the power of data in new ways, such as Facebook or The Guardian suggesting articles in your feed. This amazing technology helps computer systems learn and improve from experience by developing computer programs that can automatically access data and perform tasks via predictions and detections. Let us start by answering the two most important questions: 

What is Machine Learning and how does it work

The concept has been around for a long time, just think of the World War II Enigma Machine, for example. However, the idea of automating the application of complex mathematical calculations to big data has only been around for several years, though it’s now gaining more momentum. 

But what is Machine Learning and how does it work? Simply put, it is a core sub-area of Artificial Intelligence (AI). Like us, humans, machine learning applications learn from experience or to be accurate, data. When exposed to new data, these applications learn, grow, change, and develop by themselves. 

Generally, the learning process requires huge amounts of data that provides an expected response given particular inputs. Each input/response pair represents an example and more examples make it easier for the algorithm to learn. That’s because each input/response pair fits within a line, cluster, or other statistical representation that defines a problem domain.

In other words, ML involves computers finding insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process.

The difference between us, humans, and machine learning is the way we understand what the algorithm has learned. When humans analyze data, we build an understanding of it to a certain extent. On the other hand, learning in ML is purely mathematical, and it ends by associating certain inputs with certain outputs. It has nothing to do with understanding what the algorithm has learned. The learning process is often described as training because the algorithm is trained to match the correct answer (the output) to every question offered (the input). If you want to learn even more about this, check Machine Learning For Dummies, by John Paul Mueller and Luca Massaron, this read describes how this process works in detail and it’s perfect for those who want to master this subject. 

Read also: What is robotic process automation (RPA)? What kinds of repetitive tasks can it handle

What are the Different Types of this AI sub-area?

Now that we learned what machine learning is and how it works, let’s go even further and find out about what are the different types of this beautiful sub-area of Artificial Intelligence. Because of its complexity, ML uses two main techniques: supervised learning and unsupervised. But, keep in mind that approximately 70% of machine learning is supervised learning.

  1. Supervised Learning

It allows you to collect data or produce a data output from a previous ML deployment. Supervised learning is exciting because it works in much the same way humans actually learn. Here we present the computer with a collection of labeled data points called a training set (for example a set of readouts from a system of train terminals and markers where they had delays in the last three months). To better understand this, below you can find a list of top algorithms currently being used for supervised learning are:

  • Polynomial regression
  • Random forest
  • Linear regression
  • Logistic regression
  • Decision trees
  • K-nearest neighbors
  • Naive Bayes
  1. Unsupervised machine learning 

It helps you find all kinds of unknown patterns in data. In unsupervised learning, the algorithm tries to learn some inherent structure to the data with only unlabelled examples. Two common unsupervised learning tasks are clustering and dimensionality reduction. 

  • In clustering, we attempt to group data points into meaningful clusters such that elements within a given cluster are similar to each other but dissimilar to those from other clusters. Clustering is useful for tasks such as market segmentation.
  • Dimension reduction models reduce the number of variables in a dataset by grouping similar or correlated attributes for better interpretation and more effective model training.

Again, to understand it better, here are the top 7 algorithms currently being used for unsupervised learning are:

  • Partial least squares
  • Fuzzy means
  • Singular value decomposition
  • K-means clustering
  • Apriori
  • Hierarchical clustering
  • Principal component analysis

Machine Learning at Present

From the new industry of self-driving vehicles to exploring the galaxy, machine learning is now responsible for some of the most significant advancements in technology. This is the reason why, recently, ML was defined by Stanford University as “the science of getting computers to act without being explicitly programmed”. Plus, ML introduce us to a new array of concepts and technologies, such as supervised and unsupervised learning, new algorithms for robots, the Internet of Things, analytics tools or chatbots. So, to put it in plain English, machine learning is our present and below you can find seven of the most common ways of how the world of business is currently using machine learning:

  • Analyzing Sales Data: Streamlining the data
  • Real-Time Mobile Personalization: Promoting the experience
  • Fraud Detection: Detecting pattern changes
  • Product Recommendations: Customer personalization
  • Learning Management Systems: Decision-making programs
  • Dynamic Pricing: Flexible pricing based on a need or demand
  • Natural Language Processing: Speaking with humans

Why use machine learning? Because, combined with new computing technologies, it promotes scalability and improves efficiency. And, combined with business analytics, it can resolve a variety of organizational complexities. Our present opens all kinds of possibilities for machine learning use and our future will be no different. From outbreaks of disease to the rise and fall of stocks, modern machine learning models can be used to make predictions, helping us live a better life. So, are you ready for the future? Let’s explore the unknown and find new and amazing opportunities for humanity. Contact us and let’s start working together!