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N-gram vs. Bayesian Network: A Battle of Two Powerful Models in AI! 🤖⚔️
Decoding the Power of N-grams and Bayesian Networks: Which Model Reigns Supreme in AI?
Artificial Intelligence (AI) and Machine Learning (ML) are like the dynamic duo of the modern tech world. They help us understand patterns, predict behaviors, and make decisions from a massive sea of data 🌊📊. Two key players in this game are N-grams and Bayesian Networks — both of which come from the world of probability and statistics, but each has its unique strength and flair!
Let’s dive into the world of these two models and figure out how they work, where they shine, and why they’re both critical in AI. 🧐
What is an N-gram? 🤓
First up, let’s talk about N-grams. In the simplest terms, an N-gram is a sequence of N items (like words, letters, or symbols) from a given text or speech. If you’re thinking “hey, that sounds like a way to predict what comes next,” you’re absolutely right! 🧐
Examples of N-grams:
- Unigrams (1-gram): Just one item (e.g., “hello”, “world”).
- Bigrams (2-grams): Pairs of items (e.g., “hello world”).
