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 If you've ever tried to read an article about machine learning (ML) and felt like you needed a translator, you're not alone. ML has its own vocabulary, and once you understand the core terms, the whole field starts to make a lot more sense. This guide walks through the most important concepts, grouped so they build on each other logically rather than being a random glossary. 1. The Big Picture: What Is Machine Learning? Machine Learning is a branch of artificial intelligence where a computer system learns patterns from data instead of being explicitly programmed with rules. Rather than writing "if X, then Y" logic by hand, you feed the system examples, and it figures out the underlying pattern itself. Before diving into terms, it helps to know the three broad categories of ML: Supervised Learning — The model learns from labeled data (input paired with the correct output). Example: predicting house prices from square footage, using historical sales data where t...

Artificial Intelligence, it is the manifestation of humans making something that will work for humans, like helping them in their day-to-day tasks, such as fixing their mistakes, writing emails, scheduling meetings, writing code, fixing grammatical errors, etc. Humans always push the boundaries to achieve something that will help them to ease their job or simplify their daily activities. If we look back at our history, we can find many examples that illustrate the same. AI is one such example. Nowadays, it feels like big companies are poised to create something that we have seen in our childhood movies, like Skynet destroying the world; Terminators are everywhere; humans are controlled by AI, like we have seen in movies like The Matrix. But don't worry; there is a difference between reality and fiction. Even if not everything from the movies will be true, some parts definitely will. But let's hope for a better future. Yes, artificial intelligence technology is developed by stud...