20 AI Concepts Explained

📰 Medium · LLM

Learn 20 foundational AI concepts with plain explanations and concrete examples to overcome the terminology wall in AI and ML

beginner Published 30 Apr 2026
Action Steps
  1. Read the article to learn 20 AI concepts
  2. Explore supervised learning with labeled data input-output pairs
  3. Apply transformer models to sequence data
  4. Understand embeddings and RAG in large language models
  5. Practice implementing these concepts with concrete examples
Who Needs to Know This

Developers stepping into AI and ML can benefit from this article to understand key concepts and terminology, while data scientists and ML engineers can use it as a refresher

Key Insight

💡 Understanding foundational AI concepts is crucial for developers and data scientists to work effectively in AI and ML

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💡 Learn 20 AI concepts in one go! From supervised learning to transformers, embeddings, and RAG, get plain explanations and examples to boost your AI skills #AI #ML #LLM

Key Takeaways

Learn 20 foundational AI concepts with plain explanations and concrete examples to overcome the terminology wall in AI and ML

Full Article

Title: 20 AI Concepts Explained

URL Source: https://medium.com/@mahareddyroja247/20-ai-concepts-explained-321d0a41df1c?source=rss------llm-5

Published Time: 2026-04-30T22:13:31Z

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# 20 AI Concepts Explained. If you’re a developer stepping into AI… | by AIwithMaha | Apr, 2026 | Medium

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# 20 AI Concepts Explained

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If you’re a developer stepping into AI and ML for the first time, the terminology can feel like a wall. Loss functions, transformers, embeddings, RAG these terms get thrown around constantly, often without a clear explanation of what they actually do.

This article breaks down 20 foundational AI concepts across classical machine learning, deep learning, and large language models. Each one is explained plainly, with a concrete example instead of a vague description.

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## Classic Machine Learning

## 1. Supervised Learning

Supervised learning is a training approach where the model learns from labeled data input-output pairs where the correct answer is already known. The model adjusts its internal parameters until its predictions match the provided labels as closely as possible.

**Example:** You have a dataset of 10,000 customer emails, each labeled “spam” or “not spam.” You train a model
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