What Is Machine Learning? – Machine Learning Fundamental Concepts

What Is Machine Learning?

In simple words, machine learning is all about the use of algorithms and statistical models to analyze and carve out inferences and patterns in data, which are then used to control or develop a system that works and progresses properly without depending upon clear instructions from its user. Broadly defined as the capability of a machine to learn and imitate natural human behavior, machine learning is the backbone of any artificially intelligent application or model. Also, machine learning (ML) is a type of artificial intelligence (AI) that lets software applications get better at predicting outcomes without being explicitly programmed to do so.

Machines learn from the huge volume of data and past experiences. Machine learning tries to imitate how people learn by using data and algorithms to get better and better over time.

Along with being data consumers, we humans are also good at producing huge amounts of data every day. The emails, the daily searches, the media, and the scrolling on social media websites produce a lot of data about us nearly every single hour. This data can help us because it can be used to train machine learning models, which are good at making conclusions and smart enough to find connections between the data we give them.

Core Machine Learning Concepts

As a complete field and a huge knowledge area on its own, machine learning stands upon some core pillars that need some understanding before we can go on and fathom the depths of machine learning itself. These concepts are datasets, machine learning algorithms, machine learning workflow, and model evaluation metrics. As our goal is to figure out how machine learning works, we will now talk about and study each of the preceding features one by one.

Dataset, Features, and Labels

Without an original and ready dataset, a machine learning application proves to be nothing. A good dataset is like the fuel that flows inside the knowledge streams of a machine learning model. Without it, the model itself is useless and lifeless.