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What Is Machine Learning and Who Can Use It

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If you wanted a short or rough answer on what is machine learning, there’s plenty of information on the web. It takes a few seconds to look up the machine learning definition. Still, the only thing that matters is how well you understand what it is. This is why let’s start with the basics and go with simple explanations before we get into technical details.

But, before we do, it’s also worth mentioning that machine learning is a very promising career. Thanks to the fact that it’s rather new and makes a huge impact on our world, its popularity is only growing. Most entrepreneurs want to keep up with this cutting-edge technology, so they find and hire machine learning developers to deliver modern solutions for their businesses.

What Is Machine Learning?

Before we describe what machine learning (ML) is, let’s mention that artificial intelligence is. In simple words, artificial intelligence (AI) is It’s a technology that allows a computer (software) to act like a human.  A good example of such an AI implementation is a chatbot that replies what specific quotes regarding the type of content it receives. Basically, it’s a bot that is thought to analyze content a user sends to it. Whenever it can find specific words within the content, it knows what to do next.

What is machine learning? ML is a branch of AI and, basically, a method of data analysis. With the help of ML, you can make the same chatbot to analyze data it has to outline patterns and make decisions. Sometimes, the data is comprehensive and unpredictable which makes it hard to realize what kind of algorithms would sort it out. Still, in a lot of cases, there’s enough data that can be systemized which means a good ML algorithm can learn from it to become more precise. Probably the most popular example of machine learning consultants.

Machine Learning Algorithms

This part is more complicated but let’s try to figure this out. All ML algorithms can be divided into 3 main categories. The first one is supervised learning, the second is unsupervised learning, and the last but not least is reinforced learning. The category defines the nature of the algorithm.

Supervised Learning

In this case, we have training data and a target variable. The main idea is to create a function that can map inputs to proper output (target). Thus, the process of creating a model continues before we get a desired level of accuracy. Taking into account you have data that is labeled and using it to create a model, it is called supervised learning. There are many different supervised learning algorithms one could think of:

  • Linear regression
  • Logistic regression
  • Decision trees
  • Random forest
  • Neural networks
  • much more…

Unsupervised Learning

Here we don’t have labeled data, so the algorithm has to discover patterns on itself. This approach is focused on clustering data in various groups, detecting anomalies. The key feature of such an algorithm is its ability to discover differences and similarities in data sets. This is an advanced approach but the fact that ML models can execute without any human intervention can also have its cons. For example, this can be longer learning times, higher chances of inaccurate results, and so on. Among widely used algorithms of this category are:

  • Hierarchal clustering
  • Anomaly detection
  • Neural networks
  • Principle component analysis
  • Independent component analysis

Semi-Supervised Learning

This approach is used when some part of the given input data is labeled. Basically, it’s some sort of mix of supervised and unsupervised learning. Sometimes, it’s the best option to choose from. It’s faster and more accurate than unsupervised learning but doesn’t require all data to be labeled as it has to when supervised learning is applied.

Reinforcement Learning

In this case, everything happens through action – trial and error. This method is designed to reward desired behaviors and punish undesired. A reinforcement learning agent has to be able to understand its environment and, take action and learn to find the best (the most rewarding) path or behavior.

Deep Learning vs Machine Learning

Firstly, deep learning belongs to machine learning which belongs to artificial intelligence. Secondly, machine learning is a technology aimed to make computers think and act with minimum or no human intervention. Thirdly, deep learning is when computers use structures modeled on a human brain to learn. It is a subset of machine learning and it’s based on artificial neural networks.

Machine Learning Jobs

Thousands of companies are looking to hire a machine learning engineer. This is because it makes sense to invest in something that has the capacity to replace a lot of human work. Boring repetitive tasks and much more can be done with computers. Just imagine that you can have a soft that will replace a dozen of employees.

Obviously, it’s not that easy to find a decent machine learning developer. This is because the demand is huge and technology is new. If that’s the case, try looking in other countries. This should also help you to save not only time but the budget.


It’s outstanding how AI technology is changing everything around the world. If you want to be among those who also take advantage of it, check out these top 10 AI marketing tools for business

This information should help you to realize what ML is. Unfortunately, this is a modern technology which requires quite a technical background as well as analytical skills. This means that a regular person would have a hard time mastering something like ML. Another proof that this is a very desired career is a machine learning engineer salary alone. You can check websites like PayPal if you are interested in becoming one or hiring one.


Kossi Adzo is the editor and author of He is software engineer. Innovation, Businesses and companies are his passion. He filled several patents in IT & Communication technologies. He manages the technical operations at

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