What is machine learning and how does it work?

What is machine learning and how does it work?

Machine learning is called the most promising and challenging area of artificial intelligence. There is no single definition for machine learning yet. But most researchers put it like this: Machine learning is a branch of Artificial Intelligence and Data Science that specializes in using data and algorithms to simulate the process of human experience gaining incremental accuracy. In other words, it is the science of how to make Artificial Intelligence learn and act like a human, as well as make it continually improve its learning and abilities based on the data we provide about the real world. Machine learning uses algorithms to identify patterns in data. Based on these patterns, a data model is created for forecasting. The more data such model processes and the longer it is used, the more accurate the results become. This is very similar to how a person hones skills in practice. However, this is not an easy process. As algorithms process training datasets, more accurate models can be generated from such data. Machine learning is an important component of the growing field of data science. The adaptive nature of it makes it great for scenarios where data is constantly changing, the properties of queries or tasks are unstable, or it is virtually impossible to write code to solve it.

The link between artificial intelligence and machine learning

Artificial intelligence is a broad term that includes computer systems that mimic human thinking. The terms "machine learning" and "Artificial Intelligence" are often used in the same context, sometimes interchangeably, but they have different meanings. The difference is that machine learning always implies the use of Artificial Intelligence, however, Artificial Intelligence does not always mean machine learning.

Why is machine learning so important?

Machine learning is important because it provides companies with insight into trends in customer behavior and business operating patterns. Also, it helps and supports the development of new products. For example, many leading companies today like Facebook, Google, and Uber, are making machine learning a central part of their business. Machine learning has become an important competitive advantage for many companies.

Machine learning methods

There are two types of machine learning: use case learning, or inductive learning, and deductive learning. The last one usually refers to the field of expert systems so the terms "machine learning" and "learning from precedents" can be considered synonymous. Machine learning is divided into four main types: 

- Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.

- Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.

- Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.

- Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards.

Real-world machine learning use cases

Here are some examples of machine learning you may see and use every day:

Speech recognition. Automatic speech recognition, speech-to-text. It is a capability that uses natural language processing to process human speech into a written format. 

Customer service. It is online chatbots that replace human agents along the customer journey. They answer questions frequently, provide personalized advice, suggest sizes for users, and so on.

Computer vision. It is based on ultra-precise neural networks, is being used to tag photos on social media, X-ray imaging in healthcare, and self-driving cars in the automotive industry.

Recommendation engines. Using past consumption data, artificial intelligence algorithms can help identify data trends. This is used to provide customers with appropriate additional guidance during the checkout process for online stores.

Automated stock trading. AI-powered trading platforms execute thousands or even millions of trades per day without human intervention.

As we can see machine learning is present in all spheres of life today. Every time we use banking services, shop online, or communicate on social media, machine learning algorithms help make this interaction more convenient, more efficient, and safer. Machine learning and related technologies are evolving rapidly. Their capabilities today are just the tip of the iceberg. Mifort is an experienced IT outsourcing company. We successfully delivered more than 180 projects for the clients including machine learning. If you have any ideas we are ready to help you and make them true.