What is Machine Learning? Everything You Need to Know


Machine learning is one of many topics of interest lately, not only in the realm of business but across different industries. From autonomous cars to video recommendation systems, machine learning enables software to make sense of our complicated and unpredictable world. The technology isn’t new; however, its ability to crunch big data faster and more accurately than ever before is a recent development. 

Most people might not be aware, but machine learning has, over the years, seeped into our lives. It has and continues to make everyone’s life more efficient and enjoyable. Here are a few applications of machine learning in day-to-day life:

  • Virtual personal assistants (Alexa, Siri, Google Now, etc.)
  • Social media networks (Facebook, Pinterest, Instagram, etc.)
  • Email spam filtering (Google, Yahoo, etc.)
  • Recommendations (YouTube, Netflix, Amazon, etc.)

In this guide, you can explore what machine learning is, how it works, and why it matters in the digital era. Here, you can also discover real-life applications of machine learning.

Table Of Contents

What is machine learning?

What are the main types of machine learning algorithms?

What are the common uses of machine learning in business?

Key skill areas required in the field of machine learning

Common job roles in the field of machine learning

Real-life applications of machine learning algorithms


What is machine learning?

Artificial Intelligence and machine learning are key drivers of digital transformation. Often, the terms are used interchangeably. However, they don’t exactly pertain to the same thing. AI is a broad concept of machines that are designed to carry out tasks that normally require human intelligence. Machine learning, on the other hand, is a subset of artificial intelligence responsible for training algorithms and programs to think like a human. Machine learning consists of three components: 

  • Computational algorithm 
  • Massive volumes of data 
  • Base knowledge used to train the algorithm

The technology supports the analysis of massive quantities of data. Although it can deliver faster and more accurate results, training it often requires a lot of time and resources.

What are the main types of machine learning algorithms?

Machine learning is split into different categories based on their purpose and the way in which algorithms and programs are taught. These are the main categories:

Supervised learning

Supervised learning is the easiest to implement. This approach is similar to teaching a child using flashcards. For supervised learning, an algorithm is fed large volumes of labeled data, which allows it to comprehend the relationship between the example and its label. Eventually, it will learn how to predict the label of each example. 

If you want to hone a machine’s predictive capabilities, you start by creating a training set. It contains labeled data which the machine will need to predict outcomes. Let’s say you want to train a machine to predict the length of time it takes to drive from home to work and vice versa. If it rains, your instincts say you’ll be home late. However, the machine needs data and statistics to come to that conclusion. 

In your training set, you will need to input the total commute time and factors such as weather conditions, the time of the day, the route, etc. The machine begins to determine the relationship between the labeled data. In this case, it determines that the more it rains, the longer it takes for you to reach home. 

Unsupervised learning

Unsupervised learning is an approach used to train algorithms to determine certain patterns in the data. Labeled data is unnecessary. However, it still requires large quantities of data, along with the proper tools for comprehending the data set’s properties. By feeding an algorithm with such information, it will learn how to organize and cluster the information in categories, allowing humans to comprehend the newly organized data.

Its capacity to learn for itself, instead of being fed with information, enables it to look for patterns in the most complex data sets. Even if you record the voices of multiple people in a room with a single microphone, you can separate each voice with the use of unsupervised learning.

There are a number of areas where unsupervised learning can be used. Recommender systems like the ones YouTube and Netflix use are good examples. Often, recommender systems make use of unsupervised learning. The algorithm knows the watch history of users. By considering data from other users who have similar preferences, the algorithm can pick out what movies and shows viewers might possibly enjoy. 

Semi-supervised learning

As the name suggests, this approach combines supervised and unsupervised learning. To train algorithms using an approach, large quantities of unsupervised data is required along with smaller amounts of labeled data.

First, the labeled data is used to train the machine learning model. The partially trained model will then be used to carry out pseudo-labeling, the process of labeling unclassified information. Once that’s done, the model is fed with results of the labeled and pseudo-labeled data.

Recently, semi-supervised learning methods with generative adversarial networks have shown a lot of promise. GANs refer to a class of machine learning systems which can use labeled data to develop new data.  An example of this is Lil Miquela, a digital supermodel.

Reinforcement learning

Much like figuring out how to pay an old school video game for the very first time, reinforcement learning enables algorithms to learn by doing. It will make a bunch of mistakes until it finally gets it right. To encourage the desired action, a system of reward and punishment must be used. This training method enables the algorithm to pick out bad behaviors from good ones. In the long run, the algorithm will figure out what actions to avoid based on the mistakes it has made. 

Reinforcement learning requires an agent and an environment. A feedback loop that connects the two is also necessary. In connecting the agent to the environment, the agent must be fed with a series of actions that can affect the environment. When it comes to connecting the environment to the agent, a reinforcement signal and an updated state must be issued to the agent.

Let’s say you intend to train an agent to play a video game. The algorithm will be the agent, while the environment is the game. Each frame in the game serves as the updated state, and the score will be the reward. By connecting all of the components, the algorithm gradually learns how to play the video game.

What are the common uses of machine learning in business?

Machine learning has applications in nearly every industry. Here are a few industries that are embracing machine learning capabilities:

  • Financial services – regulation and risk analytics
  • Logistics – predictive analytics and supply chain management
  • Retail – behavioral tracking and inventory management
  • Manufacturing – condition monitoring and predictive maintenance
  • Healthcare – identifying diseases and medical imaging diagnosis

Any company that wants to capitalize on data to improve their relationship with consumers, enhance profitability, and thrive in ever-changing times can use machine learning. From retail and manufacturing to healthcare, any business with a large enough data set should be able to use machine learning to predict outcomes and attain their goals. 

Key skill areas required in the field of machine learning

Today, several companies want to embrace AI and machine learning. However, some lack the expertise to implement and support machine learning. On the bright side, companies can always invest in new employees who are knowledgeable and skilled in this domain.

Here are the skill areas required: 

  • Statistics – Often, statistical models serve as the foundation of machine learning algorithms. Statistical methods are necessary when cleaning and preparing data for modeling. In fashioning models from data, one also needs to be adept in a myriad of statistical techniques.
  • Probability – Many algorithms are used to make reliable and accurate predictions. The mathematical tools required for this are covered in the principles of probability, as well as its derivative techniques.
  • Data modeling – This domain refers to the process of creating data structures for a company’s database. It is necessary and powerful in terms of communicating the company’s business requirements.
  • Data science – An interdisciplinary field, data science involves the application of scientific methods, algorithms, and systems for the extraction of insights from structured or unstructured data.

Look for people who are knowledgeable and skilled in statistics, data science, probability, data modeling, and software engineering. Building a team composed of experts in such fields is the key to launching, running, and maintaining machine learning initiatives.

Common job roles in the field of machine learning

To implement machine learning systems, companies ought to build teams comprising of specialists. These are the people companies need to hire:

  • Data engineers – In machine learning teams, data engineers build and maintain the infrastructure required for various processes, including modeling, analysis, and predictions. They develop and make sure databases, pipelines, and all production processes are working fine. On top of that, they deploy software in complex environments.
  • Data analysts – They assess the quality of the data and monitor processes, as well as the performance of production models. Having them on the team allows senior roles to prioritize innovation. 
  • Data scientists – These professionals are in charge of the modeling process. They use the input parameters from other team leaders to comprehend the model’s business objective. Data scientists are also responsible for articulating the requirements to engineers and stakeholders. 
  • Machine learning engineers – To run machine learning initiatives, companies must hire these professionals. Machine learning engineers have knowledge and skills in applied research, data science, and coding. They oversee the infrastructure and data pipelines required for production.

Machine learning’s ability to automate, predict, and evolve is powerful. There’s no doubt about that. However, that doesn’t mean robots will be taking over the world soon. Nowadays, human operators are crucial to the development of machine learning models as they are responsible for providing context, setting parameters, and more.


Real-life applications of machine learning algorithms

Several companies are using machine learning models to improve various facets of their business. Some have been using it longer than others. Below, you will find out a few familiar brands that have deployed machine learning-powered solutions.


Google is arguably ahead of most companies when it comes to AI, machine learning, and deep learning. Perhaps it’s because they’ve invested more than any other company to develop their own solutions with this technology. For instance, Google Research uses reinforcement learning to enable AI agents to master soccer.

The tech giant also uses machine learning to improve the web browsing experience. They use it to try and understand the intent behind every query. Then, they use it to look for the best content to match that intent.


The world’s largest community-driven hospitality company uses a machine learning platform called Bighead to categorize millions of listing photos in their database. The system ensures an excellent user experience by delivering a diverse set of photos that will help them make a decision. 

Aside from that, Airbnb uses a machine learning model to figure out their hosts’ preferences in terms of accommodation requests. Every time a guest inputs a query on the Airbnb search engine, the machine determines how likely relevant hosts want to accommodate the request.

The tech giant also uses machine learning to improve the web browsing experience. They use it to try and understand the intent behind every query. Then, they use it to look for the best content to match that intent.


Pinterest uses machine learning to improve content discovery. Like Airbnb, they deal with millions of visual content on a daily basis. To sort and make sense of massive quantities of data, they’ve invested in machine learning technologies. 

Using machine learning, Pinterest was able to develop  computer vision models which can determine the subject of any photo and identify visual patterns. On top of all that, it can match them with other photos. Every time someone looks up a photo, the system can recommend similar images.


Not a lot of solutions can drive direct and immediate value for a wide range of organizations. Although machine learning can, seeing it as a corporate panacea would be a huge mistake. After all, its performance largely depends on the data it is fed with.  

Machine learning frightens a lot of companies, as many believe that it will eventually replace the human workforce. If it will, it’s not going to happen anytime soon. To launch machine learning initiatives today, companies must have the right mix of experts – machine learning engineers, data analysts, data engineers, and data scientists – who can steer the project towards the right direction.

Still curious? To see machine learning in action on the Pyramid Analytics platform, click here to watch a demonstration on-demand.