Machine Learning Terms for AI Beginners

Before diving into fine-tuning, let’s review some key terms in AI and understand what Machine Learning is, which you might have heard of at least once.

Artificial Intelligence (AI) is the implementation of human-like learning, reasoning, and problem-solving abilities in computer programs to mimic intelligent human behavior.

AI is divided into various fields, such as Natural Language Processing (NLP), which enables machines to understand and interact using human language, and Computer Vision, which processes and interprets visual data like images.

For AI to analyze patterns in the input data and generate appropriate responses, a learning process is required. The most widely used method for this is Machine Learning.

Machine Learning refers to the process of analyzing data, learning patterns from it, and building models that can make predictions or decisions on new data.

It primarily consists of methods like Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised Learning is a learning method where input data is accompanied by corresponding answers (labels). AI models are trained using this data to predict outcomes for new data.

For example, using email content and information about whether an email is spam or not, the model can be trained to predict if a new email is spam.

Unsupervised Learning uses data without answers (labels) to discover structures or patterns within the data. For instance, customer purchase data can be used to group customers with similar buying patterns.

Reinforcement Learning is a method where actions are learned through trial and error to maximize rewards. For example, a robot learning to escape a maze can be rewarded for finding the correct path and penalized for taking wrong turns, ultimately learning the optimal route.

In the next lesson, we will explore Deep Learning, another frequently mentioned AI term.