1️⃣Defining Machine Learning

Machine Learning is a field of Artificial Intelligence that deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed.

Machine Learning can be divided into three main categories: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

In supervised learning, the algorithm is trained on a labeled dataset, and it learns to predict the output for new input data. In unsupervised learning, the algorithm is not provided with labeled data and is trained to find patterns or relationships in the input data. In reinforcement learning, the algorithm learns by interacting with its environment and receiving feedback in the form of rewards or penalties. Machine Learning has a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, and predictive analytics.

Analogy:

An analogy that can be used to explain the definition of Machine Learning is to think of it as a child learning to play a game.

• Just like a child, a computer starts off not knowing how to do something (i.e. play a game).

• The child is given examples of how to play the game by a teacher or a parent (i.e. labeled data in Machine Learning).

• The child then practices playing the game and learns from its mistakes (i.e. the algorithm learns from the data).

• Eventually, the child becomes better at the game and can even come up with new strategies to win (i.e. the algorithm can make predictions or decisions without being explicitly programmed).

References:

https://ilmukomunikasi.uma.ac.id/2022/12/19/konsultan-machine-learning-valiance/